computer & information science 2014 self study
TRANSCRIPT
Computer & Information Science
2014 Self Study
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ACKNOWLEDGEMENTS
This Self-Study is the result of a collective effort by many individuals. Prof. Rajeev Raje led the
overall planning and organization process and also wrote many parts of the study. Ms. Nicole
Wittlief provided excellent editing, research, data collection and logistics support, as well as the
writing of some sections. Prof. Mihran Tuceryan and Ms. Michele Roberts also wrote some
sections of the study. I want to thank the faculty of the Department for their timely contributions,
particularly the members of undergraduate and graduate committees. Special thanks go to Prof.
Raje who chairs the Graduate Committee, Prof. Tuceryan who chairs the Undergraduate
Committee, and Ms. Michele Roberts who chairs the Service Courses Committee. I also want to
thank Ms. Beth Tidball for her initial work and Mr. Scott Orr, Ms. Emily Good, Ms. Kat Biggers,
Ms. Nancy Reddington and Ms. Judy Gersting for providing data and helpful edits. Ms. Karen
Black, Director of Program Review, provided excellent guidelines for the entire process and Ms.
Michele Trent provided coordination and managed logistics for the Review process. Much of
the data collected in this document comes from Mr. Steve Graunke, Mr. Larry Miles and Ms.
Britta Peter from the IUPUI Office of Information Management and Institutional Research. I
appreciate their support.
Shiaofen Fang, Ph.D.
Professor and Chair
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Contents Chapter 1 Introduction ................................................................................................................. 4 Chapter 2 Recommendations from 2006 Review and Departmental Responses ................... 8 Chapter 3 Institutional Context ................................................................................................. 14
Department Overview ............................................................................................................................. 16
Academic Programs ............................................................................................................................ 16
Faculty Profile...................................................................................................................................... 17
Faculty Governance ............................................................................................................................ 17
Infrastructure and Resources .............................................................................................................. 17
Chapter 4 Personnel ................................................................................................................... 18 Current Tenured and Tenure-Track Faculty ....................................................................................... 18
Joint Appointments .............................................................................................................................. 20
Non-Tenure Track Researchers.......................................................................................................... 20
Non-Tenure Track Full Time Lecturers ............................................................................................... 20
Active Emeriti and Honorary ............................................................................................................... 20
Faculty departures .............................................................................................................................. 21
Faculty Teaching Load ........................................................................................................................ 21
Faculty Development and Recruitment ................................................................................................... 21
Faculty Development .......................................................................................................................... 21
Faculty Recruitment ............................................................................................................................ 22
Faculty Accomplishments and Recognitions ...................................................................................... 22
Current Staff ............................................................................................................................................ 28
Staff Departures ...................................................................................................................................... 28
Chapter 5 Resources .................................................................................................................. 29 Fiscal Resources ................................................................................................................................. 29
Computing Resources ......................................................................................................................... 31
Research and Teaching Laboratory Space ............................................................................................ 33
Office Space ............................................................................................................................................ 33
Chapter 6 Undergraduate Programs ......................................................................................... 35 Description of Programs ...................................................................................................................... 35
Student Enrollment Data ..................................................................................................................... 37
Profile of Undergraduate Majors ......................................................................................................... 39
Advising ............................................................................................................................................... 40
Peer-Led Team Learning .................................................................................................................... 41
Assessment ......................................................................................................................................... 41
Outreach Activities .............................................................................................................................. 43
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Future Issues and Questions .............................................................................................................. 45
Broadening Participation ..................................................................................................................... 45
Chapter 7 Graduate Programs ................................................................................................... 47 Graduate student Ethics requirement ................................................................................................. 47
Master of Science (M.S.) Program ...................................................................................................... 47
Graduate Certificate Programs ........................................................................................................... 50
Doctor of Philosophy (Ph.D.) Program ................................................................................................ 50
Graduate Minor ................................................................................................................................... 52
Student Enrollment Data ..................................................................................................................... 53
Profile of Graduate Students ............................................................................................................... 54
Advising ............................................................................................................................................... 55
Assessment ......................................................................................................................................... 56
Chapter 8 Research .................................................................................................................... 58 Research Overview ............................................................................................................................. 58
External Funding ................................................................................................................................. 58
Faculty Research Groups ................................................................................................................... 59
Database, Data Mining and Machine Learning (DDMML) Group ....................................................... 59
Software Engineering, Distributed and Parallel Computing (SEDPC) Group ..................................... 65
Imaging and Visualization ................................................................................................................... 70
Networking and Security Group .......................................................................................................... 77
Education Research ............................................................................................................................ 86
Chapter 9 Service ........................................................................................................................ 90 Chapter 10 Challenges and Future Directions ........................................................................ 96 Appendices ......................................................................................................................................... 98
Disciplinary Differences of Undergraduate Computing Programs at IUPUI ........................................... 98
Bylaws ................................................................................................................................................... 100
Ethnic Minority Enrollment Data Tables ................................................................................................ 112
Undergraduate enrollment by Race/Ethnicity ....................................................................................... 112
Graduate enrollment by Race/Ethnicity ................................................................................................ 112
Undergraduate Student Learning Outcomes ........................................................................................ 113
Graduate Student Learning Outcomes ................................................................................................. 114
Undergraduate Principles of Undergraduate Learning (PUL) Assessment—Spring 2010 through Fall
2013 ...................................................................................................................................................... 115
Listing of Publications and Grants ........................................................................................................ 124
Faculty CVs ........................................................................................................................................... 170
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Chapter 1 Introduction
The Department of Computer and Information Science at the IUPUI (henceforth referred to as
“the Department”) seeks to receive, as an outcome of the review, suggestions and
recommendations from the Review Committee to improve its educational, research, and service
missions so as to maintain its ongoing upward trajectory. The last review of the Department was
in the Fall of 2006 and was an internal review. Just before that review, then Chair of the
Department (Prof. Mathew Palakal) had left to join the IUPUI School of Informatics and a new
Interim Chair (Prof. Shiaofen Fang) and Interim Associate Chair (Prof. Rajeev Raje) were
appointed by then Dean (Prof. William Bosron) of the School of Science. Subsequently in the
Fall of 2007, Profs. Fang and Raje took over the responsibilities of these two roles in a
permanent capacity. Since the past review, the Department has achieved significant progress
on all the fronts of its academic mission. For example, the faculty strength in the Fall of 2006
was 10, while it is 16 in the Fall of 2014 with one additional faculty member joining in the Spring
of 2015. Similarly, the number of undergraduate, M.S., and Ph.D. students in the Fall of 2006
were 161, 65, and 4, while the strengths for Fall 2013 are 219, 138, and 31 respectively. The
total research expenditure of the Department increased nearly 6 times over these eight years, to
over $1.2 million – and three of the faculty members (Profs. Hasan, Dundar, and Tsechpenakis)
hired since 2006 have received the prestigious NSF CAREER Awards. All these are clear signs
of a thriving department and its successful programs.
The recent upward growth of the Department has been significant, especially considering the
current decline of state and federal support for higher education. Sustaining this growth, in the
short- and long-term future, will require the Department to continue its efforts on all three fronts
of its academic mission. Hence, this review provides an opportunity to critically assess the
strengths and weaknesses of the Department, its programs, and devise a concrete plan to build
upon the past achievements and address limitations. In this context, the Department has
identified following topics and specific questions for the assessment.
Review Topics
1. The current and future size of the faculty and research areas
2. CS Education-related efforts
3. Recruitment of research students
4. Curriculum
5. Mission differentiation
6. Assessment of Learning
Specific Review Questions
The Department has, after an extensive discussion, identified the following six questions for the
Review Committee.
1. What is the appropriate size of the faculty in the Department to have the needed critical
mass to be competitive nationally in research and education? More specifically, (i) Are
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the focal areas of strength in the Department appropriate? Which areas need further
reinforcement? (ii) Should the Department establish new areas of strength outside the
existing ones? If so, which areas?
Existing areas of research focus are discussed in depth in Chapter 8 with detailed
descriptions of projects and grants, faculty involvement, collaborations, etc. to give a
sense of the current state of Department research and our research focus. This
question is also related to concerns regarding physical space allocation and fiscal
resources, which are discussed in Chapter 5.
2. Computer science education research has been a new area of emphasis nationally. How
can the Department develop a funded CS education research program that can help
inform the Department’s academic programs? Should the focus be on hiring new faculty
trained in CS education research, or develop the expertise internally?
As can be seen in Chapter 8, the Department has already begun formal efforts in
Education Research with current faculty, but this is an area we have actively sought to
grow through external hiring without much success. As much of this research is related
to course assessment and improvement efforts in undergraduate courses, some of the
current effort is also discussed in Chapter 6 in the sections on undergraduate
assessement, and this presents an area where we could potentially expand on existing
efforts in a more formal manner.
3. The lack of quality Ph.D. students and research-oriented Masters’ students has been a
bottleneck to the Department’s research programs. How can the Department more
effectively recruit research-oriented graduate students to support active research
projects?
Our graduate programs and population are discussed in detail in Chapter 7; while the
creation of the course-only M.S. program option described therein (as well as in Chapter
2 under response #1) has certainly served to increase Department enrollment generally,
it has also served in some ways to compound the problem of attracting M.S. students to
research-oriented programs, due in part to the increased time to graduation that is
typically involved with research programs compounded by the desire to enter the job
market as soon as possible. In terms of Ph.D., the informal program for application
transfer from PUWL described in Chapter 7 has so far seen success in increasing
applications to and enrollment in our program from high-quality candidates, but we are
still looking for other ways to reach out and recruit such candidates.
4. The Department’s academic curriculum has been recently revised to better represent
several new academic programs. Are the current graduate and undergraduate curricula
adequate in meeting Computer Science educational goals and industry needs? What
changes, if any, are recommended to improve the curriculum?
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Again, the undergraduate and graduate curricula, including new programs, are described
in detail in Chapters 6 and 7, respectively. Curriculum improvements and developments
are also discussed in detail in Chapter 2 within some of the responses to 2006 review
questions.
5. While IUPUI has a number of units related to computing and information technology,
computer science remains the “central” computing discipline that provides the foundation
for education and training in the broad computing field. As a computer science
department, how does the Department position and properly differentiate itself in this
complex and sometimes competing environment to better serve our students and the
community?
6. The Principles of Undergraduate Learning (PULs) undergird IUPUI’s approach to
general education for undergraduate students. Do you find evidence that the
department’s courses for undergraduates have (a) statements of expected learning
outcomes for students that incorporate the PULs, (b) means of assessing student
learning related to the stated outcomes, and (c) systematic processes for collectively
examining the assessment information and taking warranted actions designed to
improve instruction, the curriculum, and/or student support services?
Current efforts and initiatives in undergraduate assessment are discussed in some of the
Chapter 2 responses, as well as toward the end of Chapter 6. We are also actively
seeking to begin this type of assessment within the graduate programs as well, but have
not begun this effort; graduate assessment is discussed at the end of Chapter 7.
It is hoped that these questions will aid the Review Committee in providing concrete
feedback to the Department. This feedback will help the Department maintain its
ongoing growth and assist in creating a blue-print for achieving its near- and long-term
goals.
The remainder of the self-study document is organized as follows. Chapter 2 addresses
the 5 recommendations made as a result of the 2006 self-study and presents detailed
responses as to how the Department has addressed these recommendations since that
time. Chapter 3 provides “institutional context” including a history of the Department, a
brief overview of the Department’s academic programs and a brief description of the
faculty body and faculty governance. Chapter 4 provides a detailed listing of all
departmental personnel including tenured and tenure-track faculty, Lecturers, jointly
appointed faculty, Emeritus faculty and full-time staff. Information regarding teaching
loads, faculty recruitment and development, and brief descriptions of faculty
achievements since 2006 is also included in Chapter 4. Chapter 5 discusses
departmental resources in detail, in particular an overview of the Department’s finances,
computing resources and space allocation. Chapter 6 presents a detailed description of
the Department’s various undergraduate academic programs, enrollment statistics and
trends, and information about assessment and outreach activities. Chapter 7 provides a
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similar overview of the Department’s graduate academic programs, along with
enrollment and assessment information. The Department’s research profile, including
research groups and information about projects, grants and publications are found in
Chapter 8 (accompanied by more detailed information in the Appendix). Chapter 9
highlights faculty service activities since 2006, while Chapter 10 presents the conclusion
addressing current challenges and future directions. Supplemental information is found
in the Appendix.
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Chapter 2 Recommendations from 2006 Review and Departmental Responses
The Department underwent an internal review in the Fall of 2006. The Review Team made five
recommendations. These, along with the departmental responses to them, are indicated below.
1. The Department identified several important areas of research focus, developed new
graduate programs, and took steps to deliver more courses online in order to
increase future credit hours. Continue to evaluate ways to increase credit hours.
Response: The Department has undertaken several new initiatives to increase the
credit hours at all levels. Some of these efforts are briefly described below.
i) Since the 2006 self-study, the following new faculty hires have been made:
Dr. Arjan Durresi (2007): Network Architecture, Wireless Networks, Security
Dr. Yao Liang (2007): Wireless Sensor Networks, Adaptive Network Control
Dr. Murat Dundar (2008): Machine Learning, Pattern Recognition
Dr. James Hill (2009): Agile Software Engineering, Quality-of-Service, Testing
Dr. Mohammad Hasan (2010): Data Mining, Graph Mining
Dr. Gavriil Tsechpenakis (2010): Computer Vision, Image Processing
Dr. Fengguang Song (2013): High-performance Computing
Dr. Xia Ning (Fall 2014): Big Data Analytics, Data Mining
Dr. Erman Ayday (Spring 2015): Big Data, Privacy Enhancing Technologies
These new hires have developed and offered innovative graduate and
undergraduate major courses in their areas of expertise. Such offerings have
attracted new students to the Department.
ii) In Fall 2008, the Department received approval to offer 12-credit hour
Graduate Certificates in five specialized areas. The popularity of the course-
only Applied M.S. option has also facilitated increased graduate enrollment.
iii) In 2012, the Department enhanced its curriculum offerings with a Bachelor of
Arts degree in Computer Science (CS). This B.A. degree, similar to the
traditional Bachelor of Arts programs, includes a foreign language
requirement (which helps prepare CS students for participation on diverse
work teams and employment with global organizations). Additionally, in
contrast to the B.S., the B.A. has a much stronger applied focus,
requiring significant application proficiencies and concluding with an industry
internship. The required upper-level application classes replace the upper
level math and CS theory classes required in the B.S. program. The B.A.
extends the Department target market beyond traditional Computer Science
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students and was designed for students who are seeking technical leadership
positions in industry.
iv) Additionally, the Department has developed an additional certificate program
called a “Data Analytics Certificate”. This 15-credit hour certificate targets
students with majors outside of Computer Science and returning professionals
whose career paths will require fundamental competencies in data analysis.
Required coursework includes theoretical concepts, as well as applied skills in
spreadsheets, databases, and data-focused programming languages such as
R, MATLab, and SPSS. In addition to increasing enrollment beyond computing
majors, this certificate will additionally serve the Department’s mission of
broadening participation in the computing curriculum and extend computational
thinking into additional disciplines. Moreover, it allows the Department to build
faculty and programmatic relationships with other campus units. The Data
Analytics Certificate has been unanimously approved by the School of
Science, and is scheduled for university review in September 2014.
v) The General Education Core 30 went into effect for all incoming freshmen in
the Fall 2013 semester. The General Education Core 30 consists of six
competency areas that all IUPUI students, regardless of major, are required
to complete. The six competency areas are Core Communication, Analytical
Reasoning, Cultural Understanding, Life and Physical Sciences, Arts and
Humanities, and Social Sciences. As of Fall 2014, five CSCI courses have
been approved to meet the Analytical Reasoning requirement:
CSCI 23000 Computing I
CSCI-N200 Principles of Computer Science
CSCI-N201 Programming Concepts
CSCI-N207 Data Analysis Using Spreadsheets
CSCI-N211 Introduction to Databases
The Department has also increased credit hours by adding new service
courses. For example, CSCI N200: Principles of Computer Science is a
course developed within a curriculum framework established by the NSF as
part of a national initiative to broaden participation in computing. This course
identifies seven “Big Ideas” of computing -- identification purposed to “myth-
bust” some of common misconceptions by introducing non-traditional
computing students to the breadth, creativity and problem-based nature of
Computing. The Department was selected to host an early pilot for the AP
College Board Principles program targeted to launch in 2016. The delivery of
the CS Principles curriculum, via the CSCI N200 course, emphasizes
collaborative and peer-led learning techniques to better leverage student
success. The CSCI N200 was launched in the Fall 2013, and enrollment in it
has been high enough to maintain a steady course offering in every
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subsequent semester. In Spring 2015, an additional section of the CSCI
N200 will be added as part of a themed Learning Community. In addition, the
CSCI N317 (Computation for Scientific Applications) will be offered as a new
course in the Fall 2014. The course will teach computational approaches and
software tools used in data processing, data analytics, data visualization and
data mining. The course will be a core course in the new data analytics
certificate that is proposed by the Department.
2. Work closely with the Center for Teaching and Learning to gain expertise in
application and assessment of the Principles of Undergraduate Learning (PULs).
Response: The Department has implemented 6 stage SLOs (Student Learning
Outcomes) as they pertain to the undergraduate curriculum. These are:
Stage 1: Identify the program’s student learning outcomes (SLOs).
Stage 2: Link these SLOs to specific components of the program’s curriculum.
Stage 3: Identify or create methods to measure these SLOs.
Stage 4: Collect data to determine if the SLOs are being accomplished successfully.
Stage 5: Use the data collected in Stage 4 to make curricular changes.
Stage 6: Repeat Stage 4 to determine if the curricular changes were effective.
The Department has, in recent past, focused on Stages 4, 5, and 6 in particular,
since stages 1-3 had essentially already been in place for some time and were easy
to re-define. Stage 6 for the Department is to test basic understanding of students
on the computer architecture, the interrelations among structure and functionality of
hardware and software components, and understanding of the utmost necessity for
exploiting the capabilities offered by modern computer systems. The Department
has decided to use the ETS Major Field Test (MFT) to examine student learning
outcomes. The MFT is a standardized exam that covers topics in programming
concepts, discrete structures and algorithms, and computer systems, norm-
referenced to a large set of college seniors. After implementing the MFT in the
capstone course for two consecutive years, the Department started data analysis
and discussion of future improvement of the undergraduate curriculum based on the
outcomes of the MFT. The Department determined that an additional course, CSCI
48400 (Computational Theory), should be added to the core requirements. This
course, since the Spring 2010 semester, has been taught once yearly by Dr. Judith
Gersting who is a Professor Emeritus in the Department. This course includes topics
in computational theory, complexity, and algorithms. Proficiency in these areas has
been shown to be lacking in the senior-level students for the past two MFT cycles.
This deficit persists when viewing the results of the MFT as compared with the
overall group, as well as a selected peer group of universities. Since the introduction
of this course, the results on the MFTs have been better when compared with both
the peer and the national group for the past two years.
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To support enrollment growth, the Department began offering an additional, online
version of CSCI 23000 (the gateway course for majors) in Fall of 2011. An end of
semester review by the Undergraduate Committee observed an unacceptable DWF
rate of 66.7% in the online section. A review of CS educational research suggested
that a lab/recitation model called Cyber Peer Led Team Learning (cPLTL) might offer
a significant opportunity for providing improved student impact in the online section.
With the faculty approval, cPLTL was adopted in CSCI 23000 in the next online
offering (Fall 2012). Its impact on student outcomes was significant, with improved
student success each subsequent semester. By Spring 2014, the DWF rates for the
online section are comparable to those in the face to face section (as illustrated in
the following chart).
The cPLTL model was expanded to the online section of CSCI 24000 (the next
course in the curriculum), with positive results (i.e., again, comparable DWF rates
between live and online sections). The Department is currently considering the PLTL
approach for the live sections of the gateway courses, as well.
3. Address areas of overlap in course content among CIS, the School of Informatics,
and the School of Engineering and Technology.
Response: The C4 (Computing Curriculum Coordination Council) was formed in
2011 at IUPUI as a result of the Indiana University Academic Directions Initiative,
with the objective of discussing and resolving potential new curriculum proposals
among the Schools of Engineering & Technology, Informatics, and Science,
before these proposals are forwarded to the campus level for possible approval.
One of the first tasks accomplished by this Council, after much discussion, was
to agree on a Computing Discipline Differentiation document, outlining the
descriptions of each relevant unit’s mission and objectives, to be shared with
students and advisors. This document is included in the appendix. There have
0
10
20
30
40
50
60
70
80
Fall 2011 Fall 2012 Spring 2013 Fall 2013 Spring 2014
Live vs Online DWF Rate CSCI 23000 Impact
Live Online
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also been numerous courses (including two from the Department — CSCI N200:
Principles of Computer Science and CSCI N317: Computation for Scientific
Applications) and new programs that have been discussed and approved by the
C4, in order to address potential overlap before the campus-level approval
process. The Department representatives recently proposed a cap on the
number of new course/program proposals from any unit in a given time period to
prevent course proliferation and duplication. This proposal was objected to by
the representatives from other units and was tabled for possible further future
discussions.
The Department has also developed an agreement with the Department of Electrical
& Computer Engineering whereby several courses, on a regular basis, are cross-
listed with both departments (e.g., CSCI 40300: Introduction to Operating Systems,
CSCI 43600: Principles of Computer Networking, and CSCI 50400: Concepts in
Computer Organization), rather than being offered as discrete sections offered within
each department. Faculty from each department alternate in teaching these
courses. The Department is currently in discussion on a similar arrangement with the
Department of Computer and Information Technology (CIT) in the School of
Engineering and Technology.
4. The School of Science should support the Department as they grow their efforts to
collaborate with faculty in other units and establish the master’s and doctoral degree
programs.
Response: The School of Science has provided significant support to the
Department in recent years, most notably with a large increase in Research
Investment Fund fellowship (RIF) funding allocations (through matching funds given
by the Dean’s Office, which were not available in previous years). This funding has
enabled the Department to increase its support of a greater number of Ph.D. (and
some M.S. students) on Teaching Assistantships at a time when departmental
graduate enrollment has grown significantly. In addition, the School of Science has
supported departmental efforts to recruit high-quality, research-oriented international
M.S. students with the “Dean’s Scholarship” program; this program provides selected
students with a fee remission award to reduce their tuition to the in-state rate for the
duration of their 30 credit hour program. These students must maintain sufficient
academic progress (3.5 GPA) and complete the M.S. thesis option. The funding for
this scholarship is provided by the Dean’s Office. Other school-wide initiatives have
provided increasing support to the Department, including the formalized ICR (Indirect
Cost Return) agreement and the School’s tuition policy.
Additionally, the School of Science has been very supportive in recognizing the need
for hiring more faculty and staff; 9 new faculty lines have been approved and filled
since 2007 and 1 new staff position has been created and filled in 2013-14.
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5. Continued discussion should take place to consider the options that might lead to
combining portions of the School of Engineering and Technology, the School of
Informatics, CS, or those entire units into one School.
Response: This recommendation can only be addressed by the campus higher
administration and the Department is not aware of any recent attempts in this regard.
In November of 2010, the Department made a formal position statement,
unanimously approved by the faculty, to express its strong preference of staying
within the School of Science as a Purdue department.
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Chapter 3 Institutional Context
History of IUPUI and the School of Science
Indiana University established its first extension center at Indianapolis in 1916, although the first
IU course was taught in Indianapolis in 1890. The Indianapolis campus of Purdue University
grew out of World War II training programs sponsored by the Purdue University and began its
major operations in 1946. Indiana University established the Indianapolis regional campus in the
mid-1960s. In 1968, Indiana University at Indianapolis was created by the Trustees of Indiana
University, and less than a year later, in 1969, the trustees of Indiana and Purdue universities
merged their Indianapolis operations to form Indiana University–Purdue University at
Indianapolis. Indiana University was selected to administer the campus. Purdue University
brought to the merger a growing complex of degree programs and Purdue’s traditional strengths
in the physical sciences, engineering, and technology. The name of the campus was changed to
Indiana University–Purdue University Indianapolis (IUPUI) in 1992. IUPUI and IU Bloomington
are the largest of Indiana University’s eight campuses.
A restructuring of undergraduate programs at IUPUI in the fall of 1972 created three new
schools: the School of Liberal Arts (humanities and the social sciences), the School of Science
(physical, behavioral, and life sciences), and the School of Engineering and Technology. After
being housed for almost 22 years on the 38th Street Campus, the School of Science made a
historic move in two phases into two buildings on the main campus during 1991-1993.
IUPUI’s Mission
IUPUI's mission is to advance the state of Indiana and the intellectual growth of its citizens
through research and creative activity, teaching and learning, and civic engagement. By offering
a distinctive range of bachelor's, master's, professional and doctoral degrees, IUPUI promotes
the educational, cultural and economic development of central Indiana and beyond through
innovative collaborations, external partnerships and a strong commitment to diversity.
Mission of the School of Science
“The mission of the School of Science is to serve society by educating our students as
discerning citizens and leaders in productive careers, and by advancing knowledge and
understanding through basic and applied research.”
History of the Department of Computer and Information Science
The program of Computer Science began as an option in the Department of Mathematical
Sciences in 1967, shortly after the formation of IUPUI. In 1970, the M. S. in Computer Science
was proposed, and in 1974 the B. S. was proposed. Initially, these programs were closely
coordinated with the programs in the Computer Science Department at Purdue University. At
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that time, the M. S. Program at the IUPUI was qualified as "Applied" with the focus on numerical
computation. Subsequently, undergraduate program became independent and began to differ
considerably from that at Purdue University. The nature of the M. S. curriculum followed the
historical trend in the Computer Science field from primary emphasis on scientific computing to
computer systems issues, such as operating systems, languages, networks, and databases.
Also following a national trend, the Computer Science program was separated from the
Mathematical Sciences Department in 1981 to form the present Department of Computer and
Information Science. Prof. Judith Gersting was the Chair of the Department when it was
founded in 1981 and Prof. John Gersting was also a faculty member at that time. Professors
Judith Gersting and John Gersting are currently Professors Emeritus in the Department.
The Department revised the B.S. degree requirements in 2010, resulting in a curriculum that is
current and adaptable to student interests. This was accomplished by reducing the number of
required core courses to provide students more flexibility in pursuing advanced electives and
skill courses. Changes were also made in the introductory programming courses to focus more
on programing principles while deemphasizing language specific features.
The Bachelor of Arts in Applied Computer Science was added as the second undergraduate
degree program in the Fall of 2012. The B.A. offers a balance of theoretical and applied
computing coursework to prepare students for multiple pathways into the information technology
workforce. Additional coursework in the liberal arts and social sciences further enhances
communication skills and understanding of issues in other sectors of the economy.
A five year dual-degree B.S./M.S. program was introduced in the Fall of 2013. This program
allows students who are highly motivated to earn both a Bachelor of Science and Master of
Science degree in just five academic years. B.S./M.S. students complete three 500-level
courses that satisfy general/free elective requirements in the B.S., thus shortening the time to
complete their M.S. degree by one academic term. Students then complete the remaining
twenty-one credit hours of the M.S. program after admission and earn their M.S. degree in just
one year's time.
In recognition of the need to provide non-majors with a strong ability to apply computers in their
own disciplines and future professional lives, the Department broadened and continues to
expand its curricular offerings to these students and emphasizes the use of computers in
problem solving. The Certificate of Applied Computer Science is a popular option for many
students, and tends to attract students from the Schools of Business, Continuing Studies,
Informatics, Liberal Arts, and Science. Non-major classes comprise a significant portion of the
undergraduate credit hours, and serve the needs of multiple constituencies well.
In the Fall of 2008 the Department was granted permission to offer five graduate certificates to
attract potential students from local industries. These certificates are offered in: a)
Biocomputing, b) Biometrics, c) Computer Security, d) Databases and Data Mining, and e)
Software Engineering. In addition to attracting students from local industries, these certificates
also allow students to get admitted into the M.S. program after the successful completion of the
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certificate with a formal application process. The number of current students in the certificate
programs is only 1 – mainly, as many incoming certificate students prefer to switch to the MS
program after an initial advising session. The M.S. program has experienced significant growth
since 2006, particularly with the adoption of the course-only applied option.
The Department submitted a proposal to the CS Department at Purdue University and was
granted permission to offer the Ph.D. degree at the Indianapolis campus in 2003. The program
has grown significantly since 2006, with 31 current students and 5 graduates; in addition 10 new
students have joined the program in the Fall of 2014. The Ph.D. program is offered in close
cooperation with the CS Department at Purdue University West Lafayette, and students must
pass qualifying exams and follow the Purdue University policies regarding the Ph.D. study.
Departmental Mission
“Our mission is to build excellent academic programs at all levels of computer science
education and support this academic mission through strong research programs,
industrial collaborations and community relations.”
The three pillars supporting this mission are its Graduate, Undergraduate and Service Course
Programs. The dynamics of Indiana, particularly in the Indianapolis area, obligate the
Department to not only continue, but increase this effort. The Department has a key role to play
in satisfying the information technology needs of the surrounding community and guiding their
development.
Department Overview
Academic Programs
The Department currently offers a comprehensive set of degree programs, including:
Undergraduate Certificate in Applied Computer Science
Bachelor of Science in Computer Science
Bachelor of Arts in Computer Science
B.S./M.S. Combined Degree in Computer Science
Master of Science in Computer Science
Graduate Certificates
Doctor of Philosophy in Computer Science
Ph.D. Minor in Computer Science
17
Generally, the student enrollment has increased since the last review of 2006, with significant
increases in the graduate program enrollment. The popularity of new programs, such as the
B.A. and combined B.S./M.S., has also helped to not only boost the enrollment, but also to
address various key issues the Department has continued to face – e.g., attracting new
students to the Department (outside of the traditional B.S. program) and transitioning current
students from the undergraduate programs into the graduate programs. A snapshot of
Departmental enrollment showing this growth trend is shown in Table 3.1; more detailed
information about these programs is found in Chapters 6 and 7.
2007 2008 2009 2010 2011 2012 2013
Undergraduate 7,791 8,050 8,550 8,550 7,709 8,306 9,312
Graduate 904 940 1,207 1,491 1,547 1,994 2,641
TOTAL 8,695 8,990 9,757 10,041 9,256 10,300 11,953
Table 3.1: Computer Science Enrollment (by credit hours) 2007-2013
(Fall semesters)
Faculty Profile
As enrollment in the Department has grown, it has become necessary to increase the faculty
size accordingly. Since 2006, the Department has hired 9 new tenure-track faculty members
(with one joining in the Spring of 2015) and one Lecturer. The faculty members are all actively
engaged in the teaching, research and service missions of the Department. The success in
obtaining research grants has continued to increase since 2006. Chapter 4 contains information
about the faculty research, including external and internal grants awarded for the years 2007-
present, and information on faculty accomplishments and recognitions. Shortened CVs of the
faculty memebrs are included in the Appendix for further reference.
Faculty Governance
The Department is governed by a Chair, who serves as the chief administrative officer and is
responsible for the proper functioning of the educational, research and service programs of the
Department. The Chair is supported in these duties by an Associate Chair. As per the bylaws,
the Department has a number of standing committees to which faculty are either appointed or
elected to serve—the Faculty Advisory Council, the Primary Committee, the Undergraduate
Program Committee, the Service Course Committee, the Graduate Program Committee and the
Infrastructure Committee. In addition, the Department’s faculty annually elects a representative
to the School of Science Steering Committee. A copy of the Department’s bylaws is included in
the Appendix for further reference.
Infrastructure and Resources
Enrollment growth has driven the need for significant expansion in the Department’s physical
footprint and computing infrastructure. The details of these two critical aspects are provided in
Chapter 5.
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Chapter 4 Personnel
Current Tenured and Tenure-Track Faculty
The Department has grown significantly since 2006, with several successful searches resulting
in the hiring of 9 new tenure-track faculty members (2 who will be starting later this year).
Following is the list of current tenured and tenure-track faculty; more detailed CVs of the faculty
members are included in the Appendix.
Ayday, Erman, Assistant Professor of Computer and Information Science (Expected
Start: January 2015); B.S., 2005, Middle East Technical University; M.S., 2007, Georgia
Institute of Technology; Ph.D., 2011, Georgia Institute of Technology. Specialties: Big
Data, Privacy Enhancing Technologies, Cryptography and Wireless Network Security.
Al Hasan, Mohammad, Assistant Professor in Computer and Information Science
(2010); B.Sc., 1998, Bangladesh University of Engineering and Technology; M.S., 2002,
University of Minnesota; Ph.D., 2009, Rensselaer Polytechnic Institute. Specialty: Data
Mining.
Dundar, Murat, Associate Professor of Computer and Information Science (2008); B.Sc.,
1997, Bogazici University, Turkey; M.S., 1999, Ph.D., 2003, Purdue University.
Specialties: Machine Learning, Pattern Recognition.
Durresi, Arjan, Professor of Computer and Information Science (2007); B.S., 1986, M.S.,
1990, Ph.D., 1993, Polytechnic University of Tirana, Albania. Specialties: Network
Architectures, Wireless Networks, Security.
Fang, Shiaofen, Chair and Professor of Computer and Information Science (1996); B.S.,
1983, M.S., 1986, Zhejiang University, China; Ph.D., 1992, University of Utah.
Specialties: Computer Graphics and Visualization.
Hill, James, H., Assistant Professor of Computer and Information Science (2009); B.S.,
2004, Morehouse College; M.S., 2006, Ph.D., 2009, Vanderbilt University. Specialties:
Agile Software Engineering, Quality of Service.
Liang, Yao, Professor of Computer and Information Science (2007); Ph.D., 1997,
Clemson University. Specialties: Adaptive Network Control/Resource Allocation,
Wireless Networks, Network QoS.
Mukhopadhyay, Snehasis, Professor of Computer and Information Science (1995); B.E.,
1985, Jadavpur University, Calcutta; M.E., 1987, Indian Institute of Science, Bangalore;
M.S., 1991, Ph.D., 1994, Yale University. Specialties: Intelligent Systems, Information
Management.
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Ning, Xia, Assistant Professor of Computer and Information Science(August 2014); B.S,
2005, Zhejiang University; M.S., 2009, University of Minnesota, Ph.D., 2012, University
of Minnesota. Specialties: Big-Data Analytics, Data Mining, Machine Learning, Chemical
Informatics.
Raje, Rajeev R., Professor of Computer and Information Science (1996); B.E., 1984,
University of Bombay, India; M.S., 1994, Ph.D., 1994, Syracuse University. Specialties:
Distributed Processing and Programming, Object-Oriented Design and Programming,
Component-Based Programming.
Song, Fengguang, Assistant Professor of Computer and Information Science (2013);
Ph.D., 2009, University of Tennessee at Knoxville. Specialties: High Performance
Computing, Advanced Manycore Computer Architectures, Runtime Systems, Automated
Performance Analysis and Optimization.
Tsechpenakis, Gavriil, Associate Professor in Computer and Information Science (2010);
Diploma, 1999, Ph.D., 2003, National Technical University of Athens Greece.
Specialties: Computer Vision, Image Processing.
Tuceryan, Mihran, Professor of Computer and Information Science (1997); B.S., 1978,
Massachusetts Institute of Technology; Ph.D., 1986, University of Illinois. Specialties: 3D
Computer Graphics and Visualization, Augmented Reality/Virtual Reality, User
Interfaces, Image Processing and Computer Vision, Pattern Recognition.
Xia, Yuni, Associate Professor of Computer and Information Science (2005); B.S., 1996,
Huazhong University of Science and Technology; M.S. 2002, Ph.D., 2005 Purdue
University. Specialties: Databases, Data Mining.
Zheng, Jiang Y., Professor of Computer and Information Science (2001); B.S. Comp.
Sci., 1983, Fudan University, China, M.S., 1987, Ph.D., 1990, Control Eng., Osaka
University, Japan. Specialties: Computer Vision, Image Processing, Computer Graphics,
Virtual Reality, Robotics.
Zou, Xukai, Associate Professor of Computer and Information Science (2003); B.S.,
1983, Zhengzhou University; M.S., 1986, Huazhong University of Science and
Technology; Ph.D., 2000, University of Nebraska-Lincoln. Specialties: Cryptography,
Network Security, Secure Electronic Voting, Health and Genomic Data Security and
Privacy
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Joint Appointments
Chen, Yue (Jake) (5% Appointment), Associate Professor of Computer and Information
Science and Informatics (2004); B.S., 1995, Peking University, China; M.S., 1997, Ph.D.,
2001, University of Minnesota-Twin Cities. Specialties: Bioinformatics, Data
Warehousing, Data Mining.
Palakal, Mathew J. (5% Appointment), Executive Associate Dean, IU School of
Informatics (IUPUI) and Professor of Computer and Information Science (1988); B.
Comp. Sci., 1979, M. Comp. Sci., 1983, Ph.D., 1987, Concordia University, Canada.
Specialties: Artificial Intelligence, Bioinformatics, Pattern Recognition, Artificial Neural
Networks.
Non-Tenure Track Researchers
Liu, Jing-Yuan, Research Assistant Professor (2011); B.Sc., 1995, Shandong University,
China; M.Sc., 1998, Chinese Academy of Sciences, China; Ph.D., 2004, Indiana
University. Specialties: Computational Biology and Bioinformatics.
Non-Tenure Track Full Time Lecturers
Acheson, Lingma L., Lecturer in Computer and Information Science (2007); M.S., 2004,
Purdue University. Specialties: Databases, Web Development.
Harris, Andrew J., Senior Lecturer in Computer and Information Science (1995); B.S.,
1990, M.S., 2003, Indiana University-Purdue University Indianapolis. Specialties:
General Computing, Multimedia and Game Programming.
Roberts, Michele S., Senior Lecturer in Computer and Information Science (1998); B.S.,
1976, Central College; M.S., 1978, Indiana State University; M.B.A., 1994, Indiana
Wesleyan University. Specialties: Application Courses for Non-majors, Web Authoring,
Java, Client/Server Programming, Program Management, Object-Oriented Design.
Active Emeriti and Honorary
Chin, Raymond C.Y., Professor Emeritus of Computer and Information Science (2014)
Past Chair of Computer and Information Science; B.A.E., 1962, Rensselaer Polytechnic
Institute; M.A.E., 1964, Rensselaer Polytechnic Institute; Ph.D., 1970, Case Western
Reserve University. Specialties: Numerical Computation, Scientific Computing.
Gersting Jr., John M., Professor Emeritus of Computer Science (2011); B.S., 1962,
Purdue University; M.S., 1964, Ph.D., 1969, Arizona State University. Specialties:
Databases, Computer Science Education.
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Gersting, Judith L., Professor Emeritus and Chair of Computer Science (2011); B.S.,
1962, Stetson University; M.S., 1964, Ph.D., 1969, Arizona State University. Specialties:
Computer Education.
Olson, Andrew M., Associate Professor Emeritus of Computer and Information Science
(1984); B.S., 1959, University of Wyoming; M.S., 1961, University of Wisconsin; D.Sc.,
1969, Washington University. Specialties: Computational Mathematics, Advanced
Computing Environments, Software Engineering.
Faculty departures
Several faculty members have left the Department since the last review, as listed below.
Dr. Yuanshun Dai, Assistant Professor, resigned June 2007
Dr. Jeffrey Huang, Assistant Professor, did not receive tenure, August 2007
Mr. Jeffrey Allen, Lecturer, resigned August 2008
Mr. Robert Molnar, Lecturer, resigned August 2007
Mr. Dale Roberts, Lecturer, not reappointed effective August 2010
Faculty Teaching Load
The regular teaching load for the tenure-track/tenured faculty is three courses per academic
year. The new faculty members are allowed a course bye for the first two years so that they can
establish their research programs as quickly as possible. There is a possibility of buying out of
the course commitment, within the framework provided by the School of Science, with a mutual
agreement between the faculty member and the Chair as indicated in the next section. The
typical teaching load for lecturers is four courses per semester and two courses in summer (10
per year). Starting Fall 2014, the teaching load of the Lecturers is reduced to 8 courses per year
-- some of these courses are taught in an online format.
In addition, the Department does employ the services of adjunct and part-time instructors
especially for non-major courses. These instructors are chosen after a careful process. This
process is typically coordinated by the Chair of the Service Course Committee.
Faculty Development and Recruitment
Faculty Development
Faculty are provided annual development opportunities including start-up funds for new faculty,
release time for research, teaching and research awards, and support for travel to conferences,
seminars, and workshops. Start-up funds are provided as part of the initial hiring package and
22
are valid for a period of three years after initial hire; faculty may use these funds to pay summer
salary (up to two months), support students and to support equipment purchases or travel. The
Department allocates an amount to each faculty on a yearly basis to support travel; the amount
varies annually according to the available budget but is generally $1,000 to $1,500 per year per
faculty.
The Department adopted a set of Course Buyout Guidelines in September 2009 that sets policy
for research release. As per this policy, with the approval of the Department Chair a tenured or
tenure-track faculty has the option of buying out one course per academic year, at a cost of
16.7% of his/her regular 10-month salary charged to either an external research grant or internal
grant that was specifically budgeted for course buyout. A faculty member subject to this policy
is required to teach a minimum of one course per academic year.
Faculty Recruitment
The Department has completed several successful faculty searches since the last review — in
2007 (Drs. Durresi and Liang), 2008 (Dr. Dundar), 2010 (Drs. Hasan and Tsechpenakis) and
2013 (Dr. Song). Two new faculty members have also been hired at the completion of a faculty
search this year (2014); one has started in August 2014 (Dr. Ning) and the other will be starting
in January 2015 (Dr. Ayday). Additionally, Dr. James Hill was successfully recruited from the
Vanderbilt University as an Assistant Professor in 2009. Faculty searches follow the Search
and Screen procedures set forth by the Office of Equal Opportunity and are coordinated by the
Search Committee that the Chair appoints before each search.
Faculty Accomplishments and Recognitions
Ms. Lingma Acheson, Lecturer
Since 2007, Ms. Acheson has worked extensively in international program development,
establishing a 2+2 joint degree program with Sun Yat-sen University and a 3+2 student transfer
program with Changzhou Institute of Technology, as well as a joint research program between
the IUPUI Computer Science, the IUPUI Department of Tourism and Convention Management
and Ball State University. In 2009, Lingma was the recipient of the IUPUI Glenn W. Irwin, Jr.,
M.D. Experience Excellence Award, given to faculty members whose service goes “above and
beyond the call of duty.” She has also worked significantly on course development, revising
many of the department’s N-series courses and receiving a 2013 Curriculum Enhancement
Grant from the IUPUI Center for Teaching and Learning for revising N431.
Dr. Erman Ayday, Assistant Professor (Expected to join in January 2015)
Dr. Ayday's research interests include privacy-enhancing technologies (including big data and
genomic privacy), wireless network security, game theory for wireless networks, trust and
reputation management, and recommender systems. He is the recipient of 2010 Outstanding
Research Award from the Center of Signal and Image Processing (CSIP) at Georgia Tech and
2011 ECE Graduate Research Assistant (GRA) Excellence Award from Georgia Tech. He is a
member of the IEEE and the ACM.
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Dr. Jake Chen, Associate Professor
Since 2007, Dr. Chen has co-authored numerous peer-reviewed publications and received
several awards including many from NIH, NSF, IU MURI and IUCRG; he has also been
awarded several patents. He is the Founding Director (2007) of the Indiana Center for Systems
Biology and Personalized Medicine and since 2012 has served as a Visiting Professor at the
Zhejiang Institute of Biopharmaceutical Informatics and Technology.
Dr. Murat Dundar, Associate Professor
Since 2007 Dr. Dundar has co-authored 23 peer-reviewed publications and has received five
awards including the prestigious NSF CAREER award. He has served as a PC member for
ACM SIGKDD, IEEE ICDM, SIAM SDM conferences and as a panelist for NIH and NSF review
panels. He is the main author of the paper that received the best scientific paper award in the
Bioinformatics and Biomedical Applications track at the 20th International Conference on
Pattern Recognition (ICPR'10).
Dr. Arjan Durresi, Professor
Dr. Durresi’s research focuses on networking, security and trust. He is particularly interested in
new network architectures as response to the changing challenges and needs of users in
various environments and applications. Since 2007, he has co-authored 144 peer-reviewed
publications, two of which have received Best Paper Award (at IEEE International Conference
on Digital Ecosystems and Technologies - DEST 2010, and the 6th International Conference on
Advances in Mobile Computing & Multimedia MoMM2008). He has been keynote speaker in
conferences such as IEEE AINA 2007 and NBiS 2008. Since 2007, he has been the PI of four
NSF funded research projects and chair of several international conferences, including 13th
International Conference on Network Based Information Systems – NBiS 2010, 23rd IEEE
Advanced Information and Networking Applications Conference – AINA 2009, International
Conference on Availability, Reliability and Security ARES 2009 and founder of four ongoing
workshops.
Dr. Shiaofen Fang, Professor and Chair
Since 2007, Prof. Fang has published 34 peer reviewed research papers and has received 4
external research grants from NIH and DoD as PI or co-PI totaling nearly $3.4 million. He was
also the funding director of the IUPUI Signature Center for Biocomputing which was established
in 2007 through a campus Signature Center internal grant. Prof. Fang’s research since 2007
has been focused on two major areas: (1) 3D image analysis for medical applications; and (2)
Information visualization for Health and social network applications. He has collaborated with
many health and biomedical researchers on projects such as 3D facial image analysis for fetal
alcohol syndrome diagnosis; shape analysis for neuroimaging; and large scale healthcare data
visualization.
Mr. Andrew Harris, Senior Lecturer
Mr. Harris has taught the CSCI 23000 and 24000 courses for several years and has been
involved in extensive innovation in these integral majors’ courses, including ongoing work in
distributed education and live versions of the courses, and incorporation of a peer-led team
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learning model to improve DWF rates and student retention. Mr. Harris is a well-known author
of computing books, including a number of books in the famous “Dummies” series and game
development books featuring Flash, HTML5, and Python. He is also the author of a number of
well-known open-source tools, including the Miracle language, two game engines, and a
database integration tool. He has taught internationally in Macedonia and China, and is
currently leading a project teaching children how to cope with Type I Diabetes through iPad
games. This grant is in conjunction with Riley Children’s Hospital.
Dr. Mohammad Hasan, Assistant Professor
Since 2007 Dr. Hasan has co-authored 25 peer-reviewed publications and has received four
awards including the prestigious NSF CAREER award in 2012. He has served as a PC Chair of
BIOKDD’10 Workshop, and PC member for ACM SIGKDD, IEEE ICDM, SIAM SDM, PKDD,
ACM CIKM, IEEE BIBM, IEEE BigData conferences in different years. He also served as a
panelist for NSF review panels, and reviewed grant applications submitted to Louisiana Board of
Regents and Netherland organization of Scientific Research. He is the first author of the paper
that received the best scientific paper award in the Thirteenth Pacific-Asia Conference on
Knowledge Discovery and Data Mining (PAKDD’09). In 2010, he won the SIGKDD doctoral
dissertation award. In 2013 he won IUPUI School of Science Pre-tenure research award. He
has given technical tutorials on graph sampling methodologies in top-tier data mining
conferences, such as, SIGKDD, and ICDM.
Dr. James Hill, Assistant Professor
Since joining IUPUI in 2009 as an Assistant Professor, Dr. Hill has raised more than 1 million
dollars in competitive research funds. He has also published more than 40 peer-reviewed
publications (i.e., journals, conference, workshop, book chapters, and abstracts). Dr. Hill has
graduate 3 M.S. students, and will be graduating 1 Ph.D. student. In 2010, Dr. Hill was invested
to participate in the Air Force Research Lab (AFRL) Summer Faculty Program. Due to the
applied nature of the research, Dr. Hill has transitioned several research artifacts into industrial
practice. Most notably, his research on system execution modeling and early performance
testing, realized a tool called CUTS, is to be used in the research labs of the Australian Defense
Science and Technology Organization (DSTO). Dr. Hill has severed as a invited reviewed for
many professional journals, and has assisted in organized several top conferences in Computer
Science, such as OOPSLA (now called SPLASH) and MODELS, and has served on the
Program Committee of several international conferences, such as MODELS and SOSE.
Dr. Yao Liang, Professor
Since 2007, Dr. Liang has co-authored/authored 38 journal and conference papers (full paper
peer reviewed), received 7 federal grant awards (about $1.4M external funding brought into the
university), and received 1 U.S. patent. He was a co-author of the paper received Outstanding
Student Paper Award from American Geophysical Union, 2009.
Dr. Jingyuan Liu, Research Assistant Professor
Dr. Liu was trained both as a molecular/structural biologist during her graduate studies and
computational biologist during her postdoctoral training. Before she joined CS in 2011, she
25
performed in-silico drug screening and successfully identified inhibitors/enhancers targeting
various proteins. Her research interest is in protein-protein interaction and drug development and
repurposing. She was awarded a pilot grant which allowed for the identification of the
dimerization core entity in protein-protein interactions and study the dimerization mechanism of
protein in 2012. She was awarded another pilot grant to develop a novel approach targeting
“undruggable” oncogenic protein dimers for drug discovery in 2013. The outcome of these
internal grants enabled the awarding of a DOD grant to develop inhibitors targeting survivin to
overcome acquired taxol resistance in Prostate Cancer Chemotherapy.
Dr. Snehasis Mukhopadhyay, Professor
Since 2008, Dr. Mukhopadhyay has co-authored 26 peer-reviewed publications and has
received four awards including an NSF and an NOAA research grants. He has served as a PC
member for 5 international conferences and been on the editorial board of 2 journals. He has
been on 2 NSF review panels. In 2014, he received an Indiana University Trustees Teaching
Award.
Dr. Xia Ning, Assistant Professor (Joined in August 2014)
Dr. Ning has published 21 peer-reviewed papers in top-tier conferences (e.g., KDD, ICDM,
SDM, WWW, Recsys and AISTATS) and high-impact journals and 4 technical reports, for most
of which Dr. Ning performed as the first author. Dr. Ning also has 2 pending patent applications
and another 5 under filing. She has served as a panelist for multiple NSF review panels, a PC
member for IEEE BigData workshop, BESC, ECML/PKDD, SDM workshop, SDM, ICDM,
RecSys, BioKDD, and a reviewer for high-impact journals such as Bioinformatics, Journal of
Chemical Informatics and Modeling, Transaction of Knowledge Discovery and Engineering and
Knowledge and Information Systems. She received the Roberto Patine Scholarship from
Qualcomm CR&D in 2009, and was nominated for the University of Minnesota Grad School
Doctoral Dissertation Fellowship in 2011.
Dr. Mathew Palakal, Professor
Dr. Palakal has been actively engaged in research and he has published numerous manuscripts
in peer-reviewed venues in the areas of natural language processing (text mining) and machine
learning. As a PI or Co-PI, Dr. Palakal received over several million in grants, in support of his
research. He has presented his work at local, national and international conferences including
presenting as a keynote speaker. Dr. Palakal has been an active organizer of ACM SIGAPP
SAC, where he has served as the Program Chair for two years and Poster Chair for several
years. For his dedicated services, he received the Outstanding Service Award from ACM
SIGAPP in 2012. Dr. Palakal served as an external reviewer for Journals, numerous
conferences, the National Science Foundation, and the National Institute of Health. He is also
on the Editorial Board for the International Journal of Data Mining and Bioinformatics.
Dr. Rajeev Raje, Professor
Since 2007, Dr. Raje has co-authored thirty four publications as book chapters, book, journal
and conference articles in the fields of distributed computing, software engineering and
component-based systems. He has been involved as PI or Co-PI, since 2007, in external and
26
internal grants worth around four hundred and ninety thousand dollars. His research was also
presented, in the form of posters, on more than twenty five occasions since 2007. Dr. Raje has
also acted as an invited speaker on many occasions including a recent keynote talk in 2013 in
an international conference. Dr. Raje has served on program committees of prominent
conferences such as the EDOC, SEKE, HPCC, and SAC -- in total, more than thirty times since
2007. He has also acted as an external reviewer for journals, conferences, the NSF, and
national and international universities. Since 2007, Dr. Raje has been a recipient of several
honors, including the IUPUI Trustees Teaching Award (2010), Favorite Faculty Award,
Computer Science Club, IUPUI (2007-08) and was elected as a Senior Member of both ACM
and IEEE in 2014.
Ms. Michele Roberts, Senior Lecturer
From 2007, the scope of Ms. Robert's responsibilities grew from regular class-room teaching to
heading up the Service Committee, being responsible for the N-Series Curriculum, the
department’s DE program, and managing the part-time teaching faculty. In addition, Michele
pursued her research interest in Computer Science education, being awarded several internal
and external grants, including participation in the CS 10K Project as a pilot instructor for the new
CS Principles course and a poster presentation at the 2014 SIGSCE convention. Most recently,
Michele was promoted to Senior Lecturer and won the School of Science Outstanding Faculty
Service award.
Dr. Fengguang Song, Assistant Professor
Since 2007, Dr. Song has co-authored twelve peer-reviewed research papers in premier
international conferences such as HPDC’07, SC’09, SC’10, ICS’12, UCC’13, ICS’14. He was
awarded one IUPUI grant and one PRF travel grant in the year of 2013. He has served as a
TPC member for the prestigious SC conferences, IPDPS conferences, EuroMicro conferences
and been a regular reviewer for TPDS, JPDC, and ParCo journals. One of his papers was the
Best Paper Award runner up in the ACM/IEEE UCC’13 conference. Furthermore, he is one of
the four main contributors to Samsung’s KVCache project, which won the Gold Medal at the
Samsung Best Paper Award 2013, one of 9 Gold Medals from 1,700 submissions around the
world.
Dr. Gavriil Tsechpenakis, Associate Professor
Since 2007 Dr. Tsechpenakis has co-authored 14 journal papers, 19 peer-review conference
papers, and 3 book chapters, while most of his cross-disciplinary work has been presented in
abstracts or short papers in 8 conferences, either as oral or poster format. He has completed 6
funded projects, as a co-investigator, and 1 NSF project as principal investigator. He is the
recipient of the NSF CAREER Award (2013-2018) and the IU Collaborative Research Grant
(IUCRG, 2013; funding rate ~5%). The total budget from his grants, as PI or co-(P)I while
in IUPUI (2010--present) sums up to approximately $1.2M (amount for CIS-IUPUI, not total
grant budget).
27
Dr. Mihran Tuceryan, Professor
Since 2007, Dr. Tuceryan has authored a total of 2 abstracts, 20 peer-reviewed papers (journal
or conference proceedings), and 9 presentations, and has a total of 1,371 citations since 2009
(Google Scholar). He has received 2 grants from National Institute of Justice (1 subcontract
from Institute for Forensic Imaging) totaling $96,000 and $253,000. He has taught 5 courses for
a total enrollment of 549 since 2007, along with graduating 1 Ph.D. student and 3 M.S. thesis
students since 2007. He has served as Program Committee Member of the following
conferences: International Symposium on Mixed and Augmented Reality (ISMAR), ACM
Symposium on Applied Computing (SAC), ACM Multimedia (MM). In addition, he has been a
Member of the Scientific Working Group on Imaging Technology (SWGIT) since 2008 and has
served as Reviewer for the Research Grants Council of Hong Kong.
Dr. Yuni Xia, Associate Professor
Since 2007 Dr. Xia has co-authored 28 peer-reviewed publications and has received 7
grants/awards including the IBM real time innovation award in 2008 and IBM scalable analytics
award in 2010. She has served as a PC member for over 10 conferences and as a panelist for
NSF review panels. She is the main author of the paper that received the best demo award in
International Conference on Database Systems for Advanced Applications (DASFAA) 2011.
Dr. Jiang Yu Zheng, Professor
Dr. Zheng works in the areas of image, video, multimedia, computer vision, virtual reality,
pervasive computing, and intelligent transportation systems. His current research interests
include 3D measuring and modeling, dynamic image processing and tracking, scene
representation for various environments, intelligent vehicle, and sensor network. His research
was supported by NIJ, NICT and TOYOTA. Dr. Zheng has published 150 papers in journals and
conferences as main author and he is a senior member of IEEE.
Dr. Xukai Zou, Associate Professor
Since 2007, Dr. Zou has published 45 peer-reviewed papers including five book chapters and
one monographic book. He was awarded three external grants: one from the National Science
Foundation, one from Cisco and one from Northrup Grumman; he also has received four
internal grants. He has served as associate editor for three international journals and as
program co-chair and committee member and reviewer for several international conferences
and journals. He has also served as a NSF panelist and panel reviewer for NIH.
In addition to the regular faculty members, the Department has routinely hosted many visiting
faculty and visiting researchers since 2006. These visitors typically collaborate with one of the
research groups in The Department and also serve as a bridge for inter-university
collaborations.
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Current Staff Ms. Katherine “Kat” Biggers, Senior Administrative Secretary
Ms. Biggers joined the Department in January 2014, in a newly created position. She is
responsible for assisting with graduate admissions, purchasing and travel arrangements,
reimbursements, department marketing assistance and other administrative duties.
Ms. Emily Good, Academic Advisor & Program Coordinator
Ms. Good has just recently joined the Department, in August 2014. She is responsible for
undergraduate advising and program coordination duties such as scheduling, program
proposals, marketing, recruitment and retention and industrial partnership development.
Ms. Nancy Reddington, Secretary and Receptionist
Ms. Reddington joined the Department in 2011 and is the first point-of-contact for handling
visitors and general inquiries at the front desk and over the phone. She is also responsible for
coordinating travel arrangements, department scheduling, book orders and setting up course
evaluations.
Ms. Nicole Wittlief, Assistant to the Chair
Ms. Wittlief rejoined the Department in 2006 and is responsible for graduate program
administration/coordination and oversees the graduate admissions process. She also serves as
the Department’s fiscal officer and is responsible for all fiscal management, including HR
matters; this also includes serving as the Department’s grant coordinator, which involves
assisting faculty with proposal creation/development and post-award management.
Staff Departures The Department has had to cope with several staff departures since 2006, completing several
successful searches for new staff.
Ms. Myla Langford, Secretary and Receptionist, resigned September 2008.
Mr. Joshua Morrison, Research, Administrative and Program Coordinator, resigned May
2012.
Ms. Beth Tidball, Academic Advisor and Program Coordinator, resigned July 2014.
Ms. DeeDee Whittaker, Secretary and Receptionist, resigned February 2011.
In 2010, the School of Science centralized IT services and now includes 4 full-time staff
members who support the Department, as well as the rest of the school. The following 2 staff
members (out of 4) were formally full-time with the Department before they transitioned to the
School.
Mr. David Debon, Computer Support Specialist
Mr. Scott Orr, Network Systems Engineer
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Chapter 5 Resources
Fiscal Resources
The Department takes seriously its role as a financial steward and has enjoyed financial stability
in the period from 2007-present. Table 5.1 illustrates the academic year expenditures for
various departmental responsibilities.
2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 Graduate stipend
(includes RIF/CTE) $115,000 $113,850 $170,600 $250,126 $219,406 $274,364 $321,471
Supplies & Expense (S&E)
$79,618 $53,352 $69,785 $52,835 $21,731 $44,719 $58,666
Hourly and PT instructor
$15,816 $112,881 $95,657 $79,161 $64,402 $66,862 $90,549
Travel $19,661 $16,841 $9,237 $15,687 $13,779 $23,334 $30,373
Fee Remission $113,775 $138,723 $90,679 $20,788 $26,515 $24,223 $52,063
TOTAL $343,870 $435,647 $435,958 $418,597 $345,833 $433,502 $553,122
Table 5.1 Budget Appropriations by Fiscal Year, 2007/08 to 2013/14
General fund appropriations have increased slightly or generally held steady in the study period
and the Department has closed each fiscal year either even or with an unspent balance that
was carried over into the following year to support office renovation and supplement the
departmental lab fee escrow account, which is used to renovate teaching labs and make other
large departmental equipment purchases. The reason for the large increase in Supplies &
Expense (S&E) expenditures and travel expenses in the 2013-14 fiscal year is due to a
concerted effort to spend the entire amount of carryover from previous years; to this end,
several large renovations were undertaken (SL 280 conference room kitchen, SL 280 reception
area) and faculty were given a greater allocation of departmental travel funds to support
conference travel.
Research Investment Fund fellowship (RIF) and Commitment to Excellence (CTE) allocations
have increased significantly, with the advent of the Dean’s Office match, allowing the
Department to support the growing Ph.D. population with Teaching Assistant appointments and
thus, the increased trend in graduate stipend expenses. The Department has been more
careful in monitoring S&E expenses and these have fluctuated through the study period due to
variables such as faculty hires (which incur significant costs for moving expenses) and various
changes in how telephone and network services were charged by the UITS (University
Information Technology Services).
Hourly and Part-time instructor costs have fluctuated as well, but the ratio of part-time instructor
costs to the general fund account have decreased due both to an increase in DE funds (which
are used separately to pay for part-time instructors to teach DE courses) and a decline in the
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need to hire part-time instructors as enrollment in N-series courses has declined. Hourly worker
(e.g., tutors, graders, etc.) costs have remained steady but have increased in the last few years
with the implementation of the Peer-Led Team Learning model in CSCI 23000 and CSCI 24000
courses, as well as the opening of the CS Study Center in 2010 where tutoring was offered to
students enrolled in CS courses. The Study Center services have been discontinued effective
Fall 2014, as an internal review of the services showed that it was being significantly under-
utilized and that the PLTL model was more successful in providing assistance to students.
In the past, fee remission costs for graduate students were charged directly to the Department
and were included in the Department’s general fund allocation; as the Department’s graduate
student population is largely international, these costs were at the out-of-state rate. With the
implementation of the School of Science tuition remission policy, though the number of credit
hours supported by the Department has significantly increased across the years, the amount
charged to the Department has been reduced since the Department is now assessed only $80
per credit hour. Along with the increase in RIF/CTE funding, this change in fee remission
accounting has allowed the Department to increase Ph.D. support across the years.
Departmental Revenue (by fiscal year) is listed in Table 5.2 below.
Undergrad. Fee Grad. Fee Lab Fee ICR Distance Ed. Total 2007-08 $1,706,341 $528,161 $150,232 $21,195 $46,955 $2,452,884
2008-09 $1,893,964 $608,046 $171,522 $54,915 $87,342 $2,815,789 2009-10 $2,083,728 $916,603 $171,897 $76,621 $96,011 $3,344,860
2010-11 $2,265,032 $1,186,397 $159,980 $211,260 $84,835 $3,907,504 2011-12 $2,300,146 $1,217,029 $140,416 $234,289 $81,720 $3,973,600
2012-13 $2,645,785 $2,073,690 $81,979 $263,437 $84,992 $5,149,883
2013-14 $2,851,532 $2,261,731 $69,241 $312,118 $73,651 $5,568,273
Table 5.2 Department Revenue by Fiscal Year, 2007/08 to 2013/14
As seen from Table 5.2, the undergraduate revenue has continued to grow steadily since the
2007-08 fiscal year, with over a $1 million increase in the study period. More remarkably, the
graduate revenue has increased by around 4 times since 2007-08; this is due to the significant
growth in enrollment in both the M.S. and Ph.D. programs and the fact that this growth is largely
composed of international students who pay tuition at nearly 3 times the rates of residents. Lab
fees are currently charged at $73.00 per course; the Department conducted a reassessment of
lab fees in 2011 that resulted in some changes in which courses were charged lab fees and the
lab fee amount; this, combined with decreased enrollment in lab fee courses, has resulted in the
decreases in revenue seen above. Similarly, DE fees (currently charged at $59.30 per course)
have generally held steady but have declined in the last year due to decreased enrollment. As
faculty research awards have increased, so has the amount of ICR generated, with a significant
increase across the study period.
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The Department also incurs significant expenses, mainly in the form of faculty and staff salaries.
Table 5.3 shows the total revenue and general fund expenses by fiscal year from 2007/08 to
2013/14.
2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14
Gen. Exp. $1,921,776 $2,027,063 $2,121,742 $2,336,469 $2,491,589 $2,385,789 $3,024,585
Revenue $2,452,884 $2,815,789 $3,344,860 $3,907,504 $3,973,600 $5,149,883 $5,568,273
Table 5.3 Department revenue and expenses by fiscal year
The bulk of general expenditures are faculty and staff salaries and fringe benefits
(approximately 80%), while the remainder is composed of expenses listed above. As the
Department has grown in size, particularly with the hiring of 9 new tenure-track faculty since
2007, the salary costs have steadily increased each year. The total revenue of the Department
shows significant increases in the last several years, driven primarily by the large increase in
graduate fee revenue as described above.
Computing Resources
The Department maintains a robust computing infrastructure capable of supporting all the
department's administrative, academic, and research needs. Currently, there are over 20
servers, 5 clusters and 130 workstations deployed to meet these needs and are maintained by
the School of Science IT staff.
Core Servers
There are a number of servers which provide the key services needed to support all the
department missions. Several Dell PowerEdge Linux and Microsoft based systems provide for
much of the infrastructure, including e-mail, web, DNS, DHCP, and database services. All users,
including all students taking a Computer Science course, have remotely accessible storage
available to meet various course needs. General computing needs are provided by a Linux
based server and include compilers, a full Oracle Database installation, and scientific
applications such as Matlab and Maple. Virtual academic and research servers are deployed as
needed on the CIS VMware server. All CIS servers are also backed up via a duel-tape library
system which combined have a storage capacity of over 80 terabytes.
Academic Resources
Several Linux and Microsoft-based advanced Web programming servers enable students to
utilize commercial grade e-Commerce applications. Each platform provides a full set of
development tools, a major database system, and space on specialized web servers. Originally
developed to support specific courses, these servers are now also used by undergraduate and
graduate students for capstone projects and research.
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The Department maintains two primary teaching labs with 36 computers running Microsoft
Windows 7. Each system has a wide array of tools and applications to support all course needs.
While labs are used primarily at scheduled course times, one lab is open for student use at any
time. A third lab will be deployed in the spring of 2015 that will feature advanced software and
networking technologies to support a number of computational and wireless projects including a
HADOOP cluster and sensor-net equipments and will be intended to support mostly graduate
courses.
The Department has also made a strong commitment to distributed education, providing support
for multiple courses which have lecture content delivered completely online. Equipment can be
brought into classrooms for live lecture recording that may also be edited later for more modular
organization.
Research Centers and Resources
The Department has several research labs and centers, each with their own computing
resources. Supporting these labs are specialized servers or clusters. Jamuna is a dual-16 core
Linux server with 128GB of RAM for large shared memory computation. Pyrite is a 40-node, 640
core, and 8 GPU cluster used for massively parallel computation. This cluster also provides over
40 TB of data storage and is modeled after the University’s Big Red II cluster. The Department
also has a 10 node HADOOP cluster featuring map reduction software. For distributed software
and network performance analysis, the Department recently deployed an Emulab cluster with
both physical and virtual nodes which can be reconfigured into any network topology needed for
a given experiment. Smaller projects needing server support are deployed as needed on the
CIS VMware server.
Institute for Mathematical Modeling and Computational Science (iMMCS)
The School of Science Institute for Mathematical Modeling and Computational Science (iM2CS) is a cross-departmental school-level unit which promotes interdisciplinary research and educational activities, integrating mathematical and computational approaches to address problems arising in various areas of science, engineering and medicine. The specific goals of the iM2CS Institute are:
to foster excellence and innovation in research and education to promote multidisciplinary research endeavors across departments, schools and units
at IUPUI to create greater opportunities and increase competitiveness in seeking and procuring
extramural funding to support research, educational and outreach activities to engage undergraduate and graduate students in interdisciplinary research and
training activities to improve the quality of their IUPUI experience and add value and marketability to their degrees
The Institute has received financial support from many sources, including the School of Science at IUPUI, IUPUI’s Office of the Vice Chancellor for Research, the National Science Foundation and the National Institute of Health.
33
Staffing and IT Management
The Department’s infrastructure is currently managed by two teams as part of the School of
Science IT unit. This unit includes 4 full time staff members and 8 student interns. To keep track
of the many projects and requests, this team uses a Web-based request tracking system.
Priorities are given to each request and assigned to a staff member. The team manager
monitors this system to ensure all projects are completed quickly and satisfactorily. To aid in
managing all the servers and research systems, the IT staff have also deployed monitoring
systems which track system and service status as well as resource utilization. When
performance thresholds are reached, the IT staff members are notified digitally before problems
impact faculty, staff, and students.
Research and Teaching Laboratory Space
The Department maintains two teaching labs (as indicated above) in SL 247 and 251; since the
last review, SL 116 has been converted from a teaching lab into a research lab that currently
houses the Software Engineering and Distributed Systems research group. The Networking
and Security research group occupies SL 112 and the Imaging and Visualization research group
utilizes SL 239. 3 additional lab spaces were granted to the Department on the 4th floor of the
HITS building as a result of the creation of the Center for Biocomputing in 2008; since that time
one lab has been reassigned to the Math department. The Department retains use of labs 4044
and 4048, which house the Computer Aided Diagnosis and Knowledge Discovery Lab and the
Intelligent Database and Information Systems Lab, respectively. In 2012, the Department was
granted half of the suite located in Room 300 of the Walker Plaza building, which was formerly
occupied by the Center for Earth and Environmental Science. This space has been converted
into graduate student research space for the growing Intelligent Database and Information
Systems group.
Office Space
The main Department office in SL 280 consists of several faculty and staff offices of
approximately 125 square feet each, a reception area, a conference room/kitchen and the
Chair’s office. Additional faculty offices are located in SL 275, SL 277 and SL 239. SL 236 was
converted into an office space for the Department’s Lecturers and contains 4 cubicle-style
workspaces; three of these are occupied by the Lecturers, the fourth is used for DE recording.
Recent flood damage to this area has forced a temporary relocation of the Department’s
Lecturers and possible renovation of this space is currently pending. SL 265 and SL 263 were
recently vacated when the School of Science Technology group relocated to the new Science
Engineering and Lab Building in October 2013. These offices will be assigned to the two new
faculty who will begin in mid-2014 and early 2015.
Six additional offices were assigned to the Department on the 4th floor of the HITS Building as
part of the Center for Biocomputing (see above), however two of these have since been
reassigned to the Math department. The Department retains the use of offices numbered 4039,
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4041, 4043 and 4045. These offices are assigned to the Emeritus faculty, visiting postdocs as
needed and are also available for use by full-time faculty whose research labs are located in the
building. There is one cubicle for faculty/office use located in the recently obtained Walker
Plaza space (see above); this has been used to house visiting postdocs.
In 2012, the Department was able to obtain a small office space in SL 228 which was used to
house free tutoring service. With this service being discontinued starting in Fall 2014, the space
will be made available to Departmental Teaching Assistants for use as needed.
In summary, as the Department and its programs have grown so has been the need for more
personnel, computing and physical resources. Currently, all the resources are used at its
capacity and any further increase in the student population will necessitate the additional
allocation of personnel, computing infrastructure and space. The Department has indicated this
need to the School of Science Administration on a regular basis and the Administration has
been generally supportive of such requests.
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Chapter 6 Undergraduate Programs
Undergraduate programs are overseen by the Department’s Undergraduate Committee. The
Undergraduate Committee consists of members of the regular faculty and others named by the
Department Chair with the advice of the Faculty Advisory Council. It is responsible for advising
the faculty on matters relating to the undergraduate educational and research programs. These
include admission and retention standards, establishment and maintenance of specific
curriculum structure and academic requirements for the undergraduate major and service
course programs, the creation and cancellation of courses, and content and prerequisites of
new and existing courses.
Description of Programs
The Department offers following undergraduate degree programs:
Bachelor of Science in Computer Science,
Bachelor of Arts in Applied Computer Science,
Bachelor of Science: Biocomputing Pre-Med Concentration
B.S./M.S. Dual degree,
Certificate in Applied Computer Science,
Minors in Computer Science
Sun Yat-Sen University 2+2 Program
These programs are described briefly below. More details about these programs are available
at: http://cs.iupui.edu/undergraduate/degrees.
Bachelor of Science in Computer Science: The B.S. program in Computer Science requires a
minimum of 124 credit hours. This program follows state-of-the-art curriculum, keeping current
with the ACM Curriculum Guidelines. It is a calculus-based Bachelor’s degree program.
Students completing the undergraduate degree in Computer and Information Science will have
acquired a fundamental understanding of computing, information processing, and information
communication.
Bachelor of Arts in Applied Computer Science: The B.A. in Applied Computer Science offers a
balance of theoretical and applied computing coursework to prepare a student for multiple
pathways into the information technology workforce. The student’s complementary coursework
in the liberal arts and social sciences further enhances communication skills and understanding
of issues in other sectors of the economy. The program requires 120 credit hours including five
core courses in Computer Science that are supplemented by applied electives and rounded out
with courses in Algebra and Statistics, foreign language, communications, liberal arts and social
sciences. This program allows students flexibility in pursuing a minor or undergraduate
certificate program of their choice.
Bachelor of Science: Biocomputing Pre-Med Concentration: The B.S. degree program in
Computer Science with pre-med concentration, in addition to satisfying the requirements of a
36
B.S. degree in Computer Science, is designed to also satisfy the pre-requisite course
requirements to be considered for admission into U.S. medical schools. This program, similar to
the B.S. degree in Computer Science, follows state-of-the-art curriculum, keeping current with
the ACM Curriculum Guidelines. Students completing the undergraduate degree with the pre-
med concentration will have acquired a fundamental understanding of computing, information
processing, and information communication and will also be ready to enter the medical schools.
This program requires a minimum of 124 credit hours.
B.S./M.S. Dual degree: This B.S./M.S. program in Computer Science allows students who are
highly motivated to earn both a Bachelor of Science and Master of Science degrees in five
academic years. This is accomplished by allowing students to complete three 500-level courses
that satisfy general/free elective requirements in the B.S., thus shortening the time to complete
their M.S. degree by one academic term. Students then complete the remaining twenty-one
credit hours of the M.S. program after admission and earn their M.S. degree in just one year's
time.
Certificate in Applied Computer Science: The certificate program requires completion of 18
credit hours (six courses) with a GPA of at least 2.0. No individual grade below a C– is
acceptable for this certificate program. At least 9 credit hours in the certificate program must be
taken in the Department. The certificate program prepares students for entry-level positions in
jobs such as database management, web development, multimedia, system administration, or
user support. The certificate program introduces Computer Science principles, develops
practical skills in market-driven software applications and prepares students to be successful
with emerging technologies. Students will have the ability to solve complex problems, design
and implement algorithms, apply computer science theory to practical problems, adapt to
technological change and to program in at least two high-level languages.
Minor in Computer Science: The minor in Computer Science is aimed at undergraduate
students pursuing other majors at IUPUI and who would like to develop their problem solving,
computational thinking, and abstract reasoning skills that can be used in their chosen field of
study. There are two types of minor options offered by the Department: a) Minor in Applied
Computer Science aimed at students pursuing degrees in the social and behavioral sciences,
business, liberal arts, and health-related professions, and b) Minor in Computer and Information
Science aimed at students from math, science, and engineering disciplines.
Sun Yat-Sen University 2+2 Program: The School of Software at Sun Yat-Sen University
(SYSU) and the Department have established a collaborative program, allowing qualified
students to complete the first two years of the program at SYSU and the remaining two years at
IUPUI. Students who meet all the requirements from IUPUI will be awarded a Purdue University
Bachelor’s degree. Students who meet all the requirements from Sun Yat-Sen University will be
awarded the undergraduate graduation certificate and the Bachelor’s degree granted by Sun
Yat-Sen University. Both SYSU and IUPUI have exchanged descriptions of curricula, academic
standards, and evaluation techniques; have established equivalencies between credits earned
at SYSU and credits earned at IUPUI; and have determined that the degree programs offered
by both universities can be completed by fully qualified students within four years. The actual
37
length of study will be determined by the student’s credit-hour load per semester, and the
student’s enrollment in optional summer sessions.
Student Enrollment Data
The Undergraduate enrollment in the Department has been on the rise since 2007. Recent
undergraduate admissions data and the enrollment data are given in the following figures:
2007 2008 2009 2010 2011 2012 2013
Total Applicants 27 45 51 55 51 71 77
Total Admits 24 40 48 51 46 69 71
Total Enrolled 14 27 26 24 27 39 36
% of Applicants Admitted 89% 89% 94% 93% 90% 97% 92%
% of Admits Enrolled 58% 68% 54% 47% 59% 57% 51%
Table 6.1: Direct School of Science Undergraduate Admission Applications—B.S., B.A.,
B.S./M.S.
2007 2008 2009 2010 2011 2012 2013
Total Applicants 104 111 112 131 136 87 61
Total Admits 71 72 48 66 75 44 30
Total Enrolled 48 46 31 38 42 19 14
% of Applicants Admitted 68% 65% 43% 50% 55% 51% 49%
% of Admits Enrolled 68% 64% 65% 58% 56% 43% 47%
Table 6.2: University College CSCI Undergraduate Admission Applications
2007 2008 2009 2010 2011 2012 2013
Applied Certificate 8 8 11 12 10 11 6
B.S. in Computer Science 77 93 102 114 138 182 201
B.A. in Applied Computer
Science* 0 0 0 0 0 0 11
Dual Degree B.S./M.S.** 0 0 0 0 0 0 1
UNDERGRADUATE TOTAL 85 101 113 126 151 195 219
Table 6.3: Total students enrolled in undergraduate CS certificates and degree programs
*Program added Fall 2012 **Program added Fall 2013
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2007 2008 2009 2010 2011 2012 2013
7,791 8,050 8,550 8,550 7,709 8,306 9,312
Table 6.4: CS Undergraduate Credit Hours
2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13
Applied Computer Sci CRT 18 31 26 25 30 29 12
B.S. in Computer Science 15 13 17 6 15 16 26
TOTAL 33 44 43 31 45 45 38
Table 6.5: Undergraduate Degrees Conferred
The numbers in Tables 6.1-6.5 indicate the following trends: The total number of direct admits
into School of Science CSCI program since 2007 has steadily increased. The total number of
applicants into the Department from the University College (UC) has peaked in 2010 and 2011
and seems to be on the decline. The rate of admission from the UC has remained relatively
constant and of those admitted, either directly or indirectly, the enrollment rate has also
remained relatively constant. The total enrollment in the undergraduate programs (B.S., B.A.,
and certificate) has steadily increased since 2007. The total number of degrees conferred in that
time period has remained more or less constant. The significant increase in the total enrollment
has occurred since 2012, therefore it has not yet had an effect on the total number of degrees
conferred. The expectation is that in the next 2 years, this number (i.e., the degrees conferred)
will also increase.
Compared to competing computing units on campus, the Department has maintained a steady
trend of growth in enrollment and, as of Fall 2014, has surpassed Computer & Information
Technology as the computing unit with the highest enrollment. These trends in comparative
enrollment can be seen in the chart below.
39
Profile of Undergraduate Majors
Undergraduate student profile data is given in Table 6.6.
2007 2008 2009 2010 2011 2012 2013
Women 9% 8% 9% 14% 12% 9% 12%
Ethnic Minority 9% 11% 9% 19% 16% 16% 22%
Age 25 and Over 47% 49% 45% 38% 40% 34% 31%
Enrolled Full-Time 53% 56% 67% 70% 74% 72% 76%
Indiana Resident 94% 93% 93% 93% 89% 89% 89%
Table 6.6: Undergraduate Student Profile
Since 2005 and 2006, the percent of full-time enrolled students has steadily increased, with
them making up about 76% of the undergraduate population in 2013. The large majority of the
students (89% or more) have been Indiana residents. The percent of women undergraduate
population is very disappointing; undergraduate female enrollment has trended steadily low,
dipping beneath ten percent in 2012. From a commitment to reverse this trend, in 2012 the
department began participating in national efforts at broadening participation in Computer
Science, implementing some of the best practices shared by other universities with more
successfully diverse student bodies. In K-12 outreach activities, for example, presentation
content was modified to include materials shown to be more gender friendly to females, such as
0
1000
2000
3000
4000
5000
6000
Fall2010
Spring2011
Fall2011
Spring2012
Fall2012
Spring2013
Fall2013
Spring2014
Fall2014
Comparison of Undergraduate Credit Hours by Semester Fall 2010 - Fall 2014
Computer Science
Informatics
New Media
Computer & InformationTechnology
Electrical & ComputerEngineering
40
the global impact of computing. Female enrollment moved positively in 2013, and the
Department hopes to continue this improvement in subsequent years. Best practice adoption in
our outreach efforts and collaborative programs (such as the 2+2 program with Sun Yat-Sen
University in China) appears to have positively impacted minority enrollment, as well, with an
increase jump to over 20% in 2013. A more detailed breakdown of ethnic minority enrollment
can be found in the Appendix; it shows some success in attracting African American, Hispanic
and Asian students into our undergraduate program. The Department will continue efforts in
broadening participation, hopeful that the single data point gains can be sustained over time as
positive trending.
Advising
Undergraduate Computer Science majors directly admitted to the School of Science are
assigned to the Department’s Program Coordinator/Academic Advisor. The first meeting with
the Academic Advisor occurs during the new student orientation. The Academic Advisor
ensures incoming students have a thorough understanding of the general education and major
requirements and the dependencies involved in the Computer Science coursework.
After the initial orientation advising session, students are encouraged to meet with the Academic
Advisor at least once a semester. To help remind students of the importance of these meetings,
a registration hold is placed on first year student accounts in the middle of the first semester. In
order to register for second semester classes, students are required to meet with the Academic
Advisor to get the hold lifted. After the second semester, no advising holds are placed although
students are still encouraged to meet with the Academic Advisor prior to registering for classes.
Students admitted as pre-Computer Science majors in the University College are assigned to a
University College Academic Advisor and meet with that Advisor during new student orientation.
However, the Computer Science advisor routinely meets with these students to answer
questions, approve course selections, and assist students in the process of applying to the
School of Science.
The Computer Science Advisor also presents a few introductory topics to students enrolled in
SCI12000 Windows on Computer Science in the fall semester. This required first year
experience course is typically taught by the Department Chair, Dr. Fang, and the students are
both Computer Science and pre-Computer Science majors. This is a wonderful opportunity for
pre-Computer Science students to interact with the Computer Science Advisor and learn more
about the program. Beginning in the Fall 2014 semester, all students enrolled in this course are
required to complete a CITI (Collaborative Institutional Training Initiative) “Responsible Conduct
of Research” course, offered through IUPUI’s Office of Research Administration.
All incoming students are added to an undergraduate email list at the beginning of the semester.
This email list is used to share announcements about new classes, changes to the curriculum,
departmental events, internships, and job postings.
The Computer Science Advisor hosts a departmental open house for all undergraduate students
in the early fall. The open house is an opportunity for new students to get to know one another,
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interact with upperclassmen, and become familiar with the faculty and staff. Food is provided by
the Department and students can enter drawings for prizes.
The Department also supports a Computer Science Club (described under the Outreach
Section). The club also plays an active role in developing student interactions and informal
discussions, including student-to-student advising.
Faculty in the Department are also involved in mentoring and advising activities in a number of
other contexts such as undergraduate research and honors classes for undergraduate students.
Furthermore, if undergraduate students show interest in specific topics, they may be mentored
under the guidance of an individual faculty member in a capstone project
Peer-Led Team Learning
Peer Led Team Learning (“PLTL”) is a recitation/laboratory model developed in the 1990’s at
City College, New York, as an experiment to address the low success rate of undergraduate
Chemistry students in gateway courses. In the PLTL model, trained student peers lead small
discussion groups in collaborative learning and problem solving that supplement lecture
materials. Using the PLTL approach, impact on student learning gains, as measured by course
assessment outcomes and DWF (Drop, Withdraw and Failure) rates have proved significant,
and the PLTL model is now utilized in numerous STEM curricula, including Computer Science.
At IUPUI, the PLTL model has been successfully extended to online courses, using an
approach called cyber Peer Led Team Learning (“cPLTL”). In cPLTL, the peer led small
discussion groups occur online, using a campus wide technology platform that allows students
to meet synchronously in a video chat to discuss lecture content and solve problems.
Interestingly, the cPLTL appears to be particularly “gender friendly”, which is of particular
interest to the department mission of supporting broadening participation in the computing field.
The Department utilizes a combination of live and online PLTL groups to offer students
scheduling flexibility. Students in gateway courses register for the PLTL small group of their
choice, and then meet weekly with trained student leaders in their small groups to discuss
lecture content and complete laboratory work. Using this model, the Department has seen
significant impact on student gains and DWF rates, especially in the online sections of the
gateway courses.
Assessment
Assessment based on Principles of Undergraduate Learning (PULs): The PULs provide
a campus wide academic framework for all IUPUI students. Detailed Information about
the PULs is located at: http://www.iupui.edu/~bulletin/iupui/2012-
2014/undergraduate/principles.shtml). To support this campus wide initiative, all
departments identify the top two PUL’s addressed in each course. At semester end,
each student receives not only a course grade, but also an achievement assessment
against both identified PUL’s. As part of continuous improvement, instructors and the
various undergraduate committees review aggregated PUL assessment data and fine
tune course curriculum. As an example, in 2013 a review of PUL data for non-major
courses indicated that PUL scoring was lowest for the entry level N100 course. The
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N100 syllabus and course materials were modified to make explicit connections between
computing course content and the targeted PUL’s (Resource skills and Critical Thinking),
lab work was modified (such as the addition of an algorithm development lab that
demonstrates critical thinking skills), etc. 2014 PUL data will be examined for scoring
impact when available, and if results are positive, curriculum fine-tuning will be
implemented upward across the non-major course offerings.
The Department and the faculty have participated in the PUL assessments as part of the
campus-wide framework since Spring 2010. The detailed data collected in the Spring
2010 – Fall 2013 semester is included in the Appendix. A summary of the results of this
data is shown in Tables 6.7 and 6.8. The two tables separate the major course results
from the non-major course results, and they summarize the total effectiveness scores at
the 100, 200, 300, and 400 levels for the Major Emphasis and Moderate Emphasis
principles for each level. The effectiveness score ranges between 1 and 4 (1 = “Not
Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”).
Major Courses
Major emphasis effectiveness Moderate Emphasis effectiveness
100 level 2.74 2.74
200 level 3.35 3.30
300 level 2.41 2.51
400 level 2.69 2.62
Table 6.7: The mean effectiveness score for each level of major courses. Scale: 1 = “Not
Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Non-Major Courses
Major emphasis effectiveness Moderate Emphasis effectiveness
100 level 3.01 2.94
200 level 3.32 3.29
300 level 3.26 3.25
400 level 3.42 3.38
Table 6.8: The mean effectiveness score for each level of non-major courses. Scale: 1 = “Not
Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Assessment based on Student Learning Outcomes: In the Spring of 2011, the
Department drafted a set of Undergraduate Student Learning Outcomes; the full list of
these outcomes can be found in the Appendix. The Student Learning Outcomes
interpret the institutional PUL’s within the CS domain, articulating specific exiting
competencies a successful major in our program should acquire. In addition to ongoing
assessment of the Student Learning Outcomes through a traditional battery of
assessment tools such as exams, quizzes, homeworks, programming projects, research
papers, etc., each student is additionally assessed on the Student Learning Outcomes
by a Major Field Test (described below) administered in the senior Capstone course.
43
The Department would request input and suggestions from the review committee as to
what other assessments methods could be used.
Assessment based on Major Field Tests: Computer Science Students take the major
field test as a required component of their senior capstone class. The mean institution
score given below is based on the IUPUI peer institutions.
Mean Inst. Score
(National Level) IUPUI Score
Programming
Sub-score
Discrete Struct. &
Algo. Sub-score
Systems (Arch.,
OS, Networks, DB)
Sub-score
2008 148.4 158 70% correct
85th
percentile
40% correct
75th
percentile
54% correct
80th
percentile
2009 148.5 154 62% correct
70th
percentile
39% correct
90th
percentile
45% correct
40th
percentile
2010 149.3 160 65% correct 49% correct 50% correct
2011 149.3 155 50%
63rd
percentile
40%
70th
percentile
57%
90th
percentile
2012 147.6 157 58% correct
52nd
percentile
46% correct
89th
percentile
49% correct
75th
percentile
2013 149.6 152 53% correct
57th
percentile
44% correct
68th
percentile
42% correct
59th
percentile
Table 6.9: Comparison of Major Field Test Scores
This data indicates that majors in the Department are doing better than the national average
and are placed typically above the 60-70 percentile in most subjects compared to their national
peers. They seem to be better prepared in systems oriented subjects than either theoretical or
programming sub scores. As a result of the relatively low test scores in the theoretical
component, during the first two years, a new theory course was created (CSCI 48400 – Theory
of Computation) in Spring 2010. This course was made a core required course that has been
offered since. The performance of the students, after taking this core course, has somewhat
improved in the theory component of the test.
Outreach Activities
The Department is fully committed to building engagement and broadening participation in
computing and STEM activities with K-12 audiences. Ongoing outreach activities include the
following:
(1) Computer Science Day – The Department holds an annual Computer Science Day that
allows high-school students, teachers and parents to explore Computer Science through
guest speakers, student and industry panels, and contests. The main contest is a
programming contest managed in accordance with the ACM contest guidelines; contests
in gaming and Web design have been recently added to broaden appeal to less
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traditional students. The popularity of Computer Science Day has driven yearly
expansion of the participants; the number of entries has risen from 41 students in 2007
to 125 in 2014, including increases in the number of female and minority participants, as
well as entrants from outside of Indiana. One of the goals of this program is to attract
high quality, local high school students to ultimately enroll at IUPUI for their bachelor’s
programs. While exit surveys submitted by student participants indicate significant
levels of interest in applying to IUPUI, particularly from schools such as Pike High
School and Westfield High School, there have only been a handful of student
participants who have ultimately enrolled IUPUI after high school graduation. This is an
area where we are seeking improvement and would invite the Review Committee’s
suggestions on how to achieve a better enrollment “yield” from the Computer Science
Day event.
(2) Computer Science Club – The Department sponsors a Computer Science Club that is
part of campus student organizations. The club is open to any student interested in
Computer Science, and is an officially registered ACM organization. Among other
activities, the club explores industry opportunities, supports community outreach and
provides a sense of community for current and prospective students.
(3) Science Olympiad – The Department provides support and content to the annual
Science Olympiad as part of STEM outreach.
(4) STARS Computing Corps – The Department is an official STARS sponsor, with an
active students participating in STEM outreach within the student STARS organizational
model.
(5) InWiC (Indiana Women in Computing) – The Department founded a local InWiC chapter
to support women in computing, and now participates in a campus wide InWiT chapter.
(6) K-12 outreach – The Department supports an active K-12 outreach program targeted at
the public, private and home school organizations through sponsored clubs, visits,
programs and mentoring.
(7) Summer Camps – As part of outreach, the Department has supported various Summer
Camps for local youth, including computing camps for young women, a web
programming camp, and a bioinformatics camp.
(8) STEM outreach – In addition to outreach for Computing, the Department also supports
efforts to build computational thinking within the STEM disciplines. Coordinated with
other departments within the School of Science, the Department participates in K-12
outreach programs that encourage computing within the overall context of Science,
Technology, Engineering and Math initiatives.
(9) SPAN Support – IUPUI supports an early bridge to college program for rising K-12
talent. The SPAN (Special Programs for Academic Nurturing) program allows qualifying
students to be admitted as college students (as early as 9th grade) and earn college
credit for successfully completed academic work. Over the years, the SPAN program
has been a successful recruiting tool for undergraduate enrollment. The Department
engages in a variety of course arrangements with SPAN students including participation
in live courses, online courses, and offering courses on site to sufficient enrollment
numbers. By working closely with SPAN, the Department is able to reach many Indiana
45
students whose local schools offer little or no computing coursework; quite a few of
these students have continued to complete degrees within the Department.
Future Issues and Questions
The Department has many ongoing efforts to expand interdisciplinary programs in collaboration
with other departments in the School of Science. As a concrete initiative, the Department has
proposed to create a set of six “Computational” tracks in the B.S. in Interdisciplinary Studies
option. These tracks will have quite standardized course plans, while still allowing for some
individualized customization, to be approved by an advisor as well as the SOS Undergraduate
Educational Policies Committee. There is currently a proposal with a number of predefined
interdisciplinary programs:
1. Computational Biology,
2. Computational Chemistry,
3. Computational Earth Sciences,
4. Computational Forensic Sciences,
5. Computational Neuroscience,
6. Computational Physics.
These tracks are designed with a combination of existing courses from various departments
involved in the program.
Broadening Participation
The Department is strongly committed to broadening participation in its computing programs.
After careful consideration, the faculty identified three programmatic threads that hold particular
value for achieving a more balanced student profile. First, with the support of the Dean’s office,
the Department has integrated its outreach efforts into the broader STEM outreach efforts led at
the School level. In addition to specific computing programs, the Department has begun to
include a computational thread into the various STEM activities supported by the School. In
summer of 2014, for example, computational threads were added to the outreach programs
hosted by Earth Science and School of Education. Second, a proposed new Data track (which
is currently moving through the system for approval) will target a more balanced student
population, and the Department will be working with the School marketing organization to
advertise the new program to broadened target audiences. Third, the Department has invested
heavily in the national broadening participation efforts identified in the NSF 10K challenge (i.e.,
to help create a community of computational thinkers) with the launch of a Principles of
Computing pilot course. The CS Principles course, now in its third semester, has achieved
appreciable demographics, with approximately sixty percent women enrolled in the course.
In summary, the overall trends in undergraduate enrollment seem to be in a positive direction,
with some specific issues to be addressed. The direct admissions into the programs and the
total enrollments have increased over time. Since 2007, minority enrollment has increased from
8 to 42, and efforts are needed to make sure that this trend stays. The Department needs to
46
address the small percentage of women students enrolled in the program and make targeted
outreach efforts to overcome this limitation.
47
Chapter 7 Graduate Programs
The Department offers the following graduate programs, which are described briefly in this
chapter:
Master of Science
Doctor of Philosophy
Graduate Certificates
Graduate Minor for students pursuing other degree programs with the Indiana
University or Purdue University
The oversight of these graduate programs is entrusted to the Graduate Committee of the
Department. This Committee, typically, consists of four faculty members selected by the Chair
of the Department. One faculty member is selected as the Chair of this Committee – Prof. Raje
has served as the Chair of this Committee since 2006. This Committee routinely interacts with
the Graduate Admissions Committee and the Graduate Affairs Committee of the Computer
Science Department at the Purdue University, West Lafayette (CS-PUWL) and also with the
Graduate Office at the IUPUI campus. The detailed descriptions of these programs are available
at: http://cs.iupui.edu/graduate/degrees.
Graduate student Ethics requirement
Beginning in the Fall 2014 semester, all graduate students are required to complete a CITI
(Collaborative Institutional Training Initiative) “Responsible Conduct of Research” course,
offered through IUPUI’s Office of Research Administration.
Master of Science (M.S.) Program
In addition to teaching Computer Science fundamentals, the M.S. degree program emphasizes
research in network security, databases, bioinformatics, biometrics, intelligent systems,
visualization, Software engineering, and distributed computing.
Admission Requirements
Applicants to the graduate program must have a four-year bachelor's degree or equivalent. M.S.
applicants should have a background in the following core areas of Computer Science:
Software development experience in a high level language
Data structures and algorithms
Systems (principles of operating systems, compilers, and programming languages)
Theory (discrete math and theory of computation)
Hardware (computer architecture)
48
GPA and GRE Requirement: The applicant's record should exhibit outstanding achievement as
indicated by the grade point average for each degree over his or her entire academic record.
Applicants are expected to have a GPA of at least a 3.0 on a scale of 4.0. The record should
also demonstrate strong individual accomplishments and recommendations from independent
references. Scores on the Graduate Record Exam are not required for admissions. Applicants
seeking financial aid, however, must submit general GRE exam scores.
English Proficiency Requirements: All applicants whose native language is not English are
required to submit scores for TOEFL or IELTS. An overall TOEFL IBT score of 79 or an IELTS
band score of 6.5 is required.
Program Requirements
To receive the M.S. Degree, the applicant must be admitted as a graduate student without
provisions and complete 30 semester-credit hours of study in CSCI courses numbered 500 or
above. In addition, there is a “core”/required course component which must be satisfied as part
of the 30 credit hour program; these requirements are listed below, and differ according to
semester of initial admission.
“Core” Course Requirement (for those admitted before the Fall 2013 semester)
At least 6 of the 30 required hours must be from the following Core Courses: CSC1 503,
Operating Systems; CSCI 504, Concepts in Computer Organization; CSCI 565, Programming
Languages; CSCI 580, Algorithm Design, Analysis and Implementation.
New Required Course Guidelines (effective beginning with those admitted for the Fall 2013
semester)
Of the 30 required hours, students must select 1 course each from 4 different "foundational"
categories for a total of 12 credit hours. There are 6 categories from which to select the 4, as
listed below:
Networking and Security -- CSCI 53600, CSCI 55500
Databases and Intelligent Systems -- CSCI 54100, CSCI 54900, CSCI 57300
Visualization and Graphics -- CSCI 55000, CSCI 55200, CSCI 59000 (Image
Processing)
Software Engineering -- CSCI 50600, CSCI 50700, CSCI 59000 (Software Quality
Assurance)
Theory -- CSCI 52000, CSCI 56500, CSCI 58000
Systems -- CSCI 50300, CSCI 50400, CSCI 53700
49
M.S. Program Options
The Department offers two options, as described below, for study in the M.S. Program. Both
options are administered according to the policies established by the Department and Purdue
University.
M.S. Research Option
The objective of the research option is to develop a general knowledge of Computer
Science, deep in a specific area, and an ability to perform independent research. The
student, enrolled in this option, learns research techniques by working in close
cooperation with a faculty member while doing the thesis research. This program
requires 6-9 credit hours of thesis work and at least 21 hours of graduate level
course work. The Department offers a wide selection of courses from which the
student chooses, in consultation with the graduate advisor, in order to acquire the
background necessary for doing the thesis research. The student chooses a
sufficient number of courses to complete the remainder of his or her 30-credit
program beyond the two core courses and the six to nine credit hours of thesis.
These are identified on the formal Plan of Study, which the Graduate Committee
must approve. A formal defense and associated thesis report are necessary to certify
the quality of the thesis work.
M.S. Applied Option
The objective of the applied option is to develop in the student skills and knowledge
of the Computer Science fundamentals and an ability to apply these to practical
problems. The student has two tracks in the applied program, the project track and
the course-only track.
For project students, a project is completed, usually from her or his work
environment or internship, or a faculty member's work. Its objective is to provide
an integrative experience by applying to a complex problem of a practical nature
the theory and skills learned in the course work. The objective of the course work
is to provide breadth of knowledge to the student as well as specialized
knowledge in the areas that the project will require. The graduate of this program
is prepared to adapt and respond quickly to the employer's specialized
requirements. The Applied Program, project track, requires three to six credits of
work in a Project Course and at least 24 hours of additional graduate course
credits. The Project normally involves at least two semesters of intensive work.
The student carries out the project under the supervision of a faculty member. It
is highly recommended that there also be a mentor from the sponsoring
organization in cases that the project has a non-academic sponsor. A formal
presentation and associated report are necessary to assess the quality of the
project.
50
The course-only track requires no thesis or project, and is comprised of thirty
credit hours of course work successfully completed, including at least two core
courses or four “foundational” courses as indicated above.
Graduate Certificate Programs
In addition to the M.S. program, the Department also offers Graduate Certificates in five
specialized areas. A graduate certificate will be issued when a student has completed 12
graduate credit hours in one of the specialization areas. These areas are: i) Biocomputing, ii)
Biometrics, iii) Computer Security, iv) Databases and Data Mining, and v) Software Engineering.
After finishing the requirements for the graduate certificate, the student may opt to finish the
remaining requirements towards a M.S. degree.
Doctor of Philosophy (Ph.D.) Program
Unlike the M.S. program, the Ph.D. program is conducted in close cooperation with the
Computer Science Department at the Purdue University, West Lafayette (CS-PUWL). The
admission to the Ph.D. program is recommended by the Graduate Committee of the
Department and the final decision is made by the Graduate Admissions Committee of the CS-
PUWL. Hence, the Ph.D. program follows all the guidelines created by the CS-PUWL.
An informal program was started in the Fall 2014 semester, whereby students who were not
accepted, or were accepted without funding, by the program at West Lafayette were given the
option to pass their applications on to IUPUI; this has thus far been fairly successful, with Ph.D.
applications increasing significantly for the Fall 2014 semester, including high quality candidates
who may not have otherwise considered IUPUI. As a result, we have had our largest entering
class of Ph.D. students (10).
The Ph.D. program is of 90 credits beyond bachelors, of which at least 54 credits are reserved
for research. It also includes 1 Research Orientation Course, 2 Research Courses (CS 69900,
Research Ph.D. Thesis), Ethics Requirement, and 9 Courses (which must include Operating
Systems and Algorithms). The Advisory Committee for each Ph.D. student is comprised of three
faculty members from CS-PUWL (including a Co-Advisor) and two faculty members (including
an Advisor) from the Department. In addition, the qualifying examinations, the prelims and the
final defenses are carried out with a significant involvement of the faculty members from the CS-
PUWL. It is expected that the quality of the research of the Ph.D. students is worthy of original
publications in esteemed peer-reviewed venues.
Some of the milestones of the Ph.D. program are briefly described (in a near-verbatim manner)
below. All the relevant details are available at:
https://www.cs.purdue.edu/graduate/curriculum/doctoral.html.
Qualifying Process
51
To qualify for the doctoral program, students must pass the qualifying examination, which
consists of two parts taken in sequence. Part 1 tests a student for breadth of knowledge in
computer science and the ability to use that knowledge. Part 2 tests a student for the knowledge
and ability to conduct research.
Qualifying Examination, Part 1
The Qualifying Examination, Part 1 consists of passing a written or oral Qualifying Course
Examination (QCE) corresponding to one course from each of four different areas in the list
below:
Artificial Intelligence
Data Mining and Information Retrieval
Bioinformatics
Cryptography and Information Security
Databases
High Performance Computing and Numerical Computing
Programming Languages and Compilers
Scientific Visualization, Geometric Modeling and Graphics
Simulation and Modeling
Software Engineering
Systems and Networking
Theory
The QCE need not be taken in the same semester as the course is taken. Students may take
more than four QCEs, however QCEs may be repeated only with the permission of the graduate
committee. Students must pass part 1 by the end of their fourth semester.
QCEs are given at the end of the course. Students who wish to take a QCE must register by the
end of the 12th week of the semester. The examining committee for each QCE is appointed by
the chair of the graduate committee. The instructor of the course is normally a member.
Qualifying Examination, Part 2
Students must pass an oral examination by the end of the fourth semester. Part 2 can be taken
only after the student has completed the two research courses and passed at least three of
the four QCEs.
Students are nominated for Part 2 by their research advisor, who indicates the area of research.
The examining committee consists of three faculty members, none of whom is the student's
advisor, appointed by the graduate committee in consultation with the student's advisor. The
student must arrange with the examining committee members the date, time, and place of the
examination and secure the approval of the assistant to the head (acting for the head) to
schedule the examination.
52
Preliminary Examination
The preliminary examination tests the student's competence in a research area and readiness
for research on some specific problem. The content of the examination is at the discretion of the
examining committee. The examination may consist, for example, of a presentation by the
student of papers relevant to a research topic agreed upon by the student and the committee; or
it may consist of a proposal for thesis research; or it may involve an oral examination over the
material in appropriate courses beyond the qualifying level.
The examining committee normally consists of the student's advisory committee and an
additional member chosen by the graduate committee. The preliminary examination is to be
taken by the end of the third semester following the one in which the student completes
the qualifying process and at least two semesters before the examination on the thesis.
Thesis
The thesis must present new results worthy of publication. The student must defend the thesis
publicly and to the satisfaction of the examining committee, which normally consists of the
student's advisory committee and one additional faculty member representing an area outside
that of the thesis. The thesis should be completed by the end of the fourth semester following
the one in which the student passes the preliminary examination. The graduate committee may
grant extensions.
Graduate Minor
Overview
The objective of the graduate minor in Computer Science is to provide an opportunity for current
Indiana University or Purdue University doctoral students in other disciplines at IUPUI to learn
and use Computer Science techniques and tools to solve problems in their academic fields.
Requirements
The minor will require coursework totaling 12 graduate credit hours at the 500 level or above.
These must include one three-credit hour core course selected from the following list, and three
elective computer science courses. Additional CSCI courses at the 500 level or above, such as
independent studies, may be substituted for elective courses with the permission of a student's
faculty advisor and the Minor Program Coordinator in the Department.
Core Courses for Minor
CSCI 50300, Operating Systems
CSCI 56500, Programming Languages
CSCI 58000, Algorithm Design, Analysis, and Implementation
Approved Elective Courses for Minor
53
CSCI 50600, Management of the Software Development Process
CSCI 54100, Databases
CSCI 54900, Intelligent Systems
CSCI 55000, Computer Graphics
CSCI 55200, Advanced Graphics and Visualization
CSCI 55500, Cryptography
CSCI 57300, Data Mining
CSCI 59000 (Distributed Databases, Pattern Recognition/Data Mining, and Wireless
Sensor Networks)
Student Enrollment Data
As of Fall 2014, the Department’s graduate program has grown to nearly 200 students; the
Department’s graduate program accounts for 33% of the graduate headcount within the School
of Science. For the MS program only, the Department’s program accounts for 54% of the
School’s Master’s headcount. Graduate program enrollment is shown in Tables 7.1 and 7.2
below.
2007 2008 2009 2010 2011 2012 2013
M.S. 64 57 75 100 84 112 138
Ph.D. 5 10 14 20 22 32 30
Grad Certificate 0 1 1 3 1 3 1
TOTAL 69 68 90 123 107 147 169
Table 7.1: Graduate Enrollment
2007 2008 2009 2010 2011 2012 2013
904 940 1,207 1,491 1,547 1,994 2,641
Table 7.2: Graduate Credit Hours
2009 2010 2011 2012 2013 2014
M.S. 69% 78% 80% 72% 65% 54%
Ph.D. 80% 70% 72% 79% 66.6% 58%
Table 7.3: Graduate Acceptance Rates (fall semester)
As interest in the M.S. program has grown, applications have increased from 75 in the Fall
semester of 2009 to over 200 in Fall 2014. As Table 7.3 shows, this has resulted in a more
strict set of admission criteria being applied to applications by the Graduate Admissions
Committee.
54
A comparison of enrollment across computing units on campus, as seen in the chart below, also
confirms the Department’s trend of increasing enrollment and shows that the Department’s
graduate program is now one of the largest, rivaling the School of Informatics.
Profile of Graduate Students
As of Fall 2013, 62% of the Department’s graduate population was above the age of 25; this has
remained fairly steady since 2007, with percentages ranging from 57 to 63. As seen from the
above enrollment Table 7.1, since 2007, the number of graduate students (M.S. and Ph.D.) has
significantly increased. The increase is mainly due to the large enrollment of international
students. In addition to the local full and part-time students, currently, The Department houses
international students with representatives from India, China, Sri Lanka, Bangladesh, Colombia,
Iran, Greece, Thailand, Vietnam, and Palestine. These students increase the diversity (one of
the prime objectives of the IUPUI’s Strategic Plan) of the CS graduate programs. In addition,
there are currently around 60 female graduate students – a traditionally underrepresented group
in Computer Science; these numbers are slightly higher than the last national average reported
by the CRA of 22.6% in 2012. These numbers are generally the byproduct of the large
international composition of the graduate population, where female students generally comprise
a significant portion of those in U.S. graduate programs. Table 7.4 provides additional statistics
on the Department’s graduate student population. A more detailed breakdown of ethnic minority
enrollment can be found in the Appendix; this data, not surprisingly, indicates that the vast
majority of graduate students identify as Asian. It also indicates that the Department has not
been successful in attracting African American or Hispanic students into graduate programs.
0
200
400
600
800
1000
1200
1400
1600
Fall2010
Spring2011
Fall2011
Spring2012
Fall2012
Spring2013
Fall2013
Spring2014
Fall2014
Comparison of Graduate Credit Hours by Semester Fall 2010 - Fall 2014
Computer Science
Informatics
New Media
Computer & InformationTechnology
Electrical & ComputerEngineering
55
The Department is currently working on efforts to attract and retain more local and minority
(non-international) graduate students, in particular women, and would seek the Reivew
Committee’s suggestions in this area.
2007 2008 2009 2010 2011 2012 2013
Women 36% 34% 38% 29% 24% 29% 34%
Ethnic Minority 9% 6% 8% 11% 10% 6% 5%
Age 25 and Over 58% 63% 64% 68% 70% 54% 53%
Enrolled Full-Time 29% 25% 40% 34% 39% 54% 57%
Indiana Resident 41% 50% 42% 41% 36% 27% 28%
Table 7.4 Graduate Student Profile
A typical incoming M.S. and Certificate student has a GPA of 3.0 or better (or its equivalent in
the case of international students) and a TOEFL score (again for international students) of 79 or
better. Many of the incoming students are in the top 10% of their graduating class. Some of
these students also have a few years of industrial experience after their bachelor’s degree. The
graduating requirement for the MS students is to achieve a GPA (as indicated in their plan of
study) of 3.0 or better – a large number of students do successfully meet that requirement.
Around 5% of these students participate in the M.S. thesis or the project option and publish their
research in peer-reviewed venues.
A typical incoming Ph.D. student has a GPA of 3.5 or better (or its equivalent in the case of
international students), a TOEFL score of (again, for international students) of 79 or better and
prior research experience. Any student requesting financial assistance is required to submit
their GRE scores. A typical graduation time for a Ph.D. degree is between 5 and 6 years (after
starting the Ph.D. program). So far, six students have graduated with the Ph.D. degree and all
are employed in industry.
A typical incoming Graduate Minor student is enrolled in a Ph.D. program at the IUPUI campus
in a related discipline such as Informatics. So far, 5 students have applied for this option as part
of their academic requirements.
Advising
Each student, admitted to the M.S./Ph.D. program, is assigned an initial Advisor and thereafter,
the student may decide to keep that Advisor or change the Advisor based on his/her specific
plan and preferences.
The student populations in different graduate programs require different kinds of advising. For
example, a M.S. student enrolled in the course-only typically meets with the assigned Advisor
on an infrequent basis – mainly, to discuss the courses to be taken and the associated plan of
study. In contrast, a M.S. student enrolled in the thesis or project option will be advised
continuously throughout his/her program duration. These advising efforts will be related, both, to
56
course selections and feedbacks on the research efforts. Irrespective of their program options,
each M.S. student is associated with an Advisory Committee made up of three faculty members.
This Committee approves the plan of study for that student and in the case of thesis/project
students it acts as the Examination Committee for assessing the quality of the thesis/project
effort and decides the final outcome (i.e., pass or fail).
Each Ph.D. student has an Advisor in the Department and a Co-Advisor at the CS-PUWL. In
addition, each student is associated with an Advisory Committee and an Examination
Committee as described earlier in the Section related to the Ph.D. programs. Each Ph.D.
student is continuously advised by these Committees and works in close cooperation with the
Advisor and the Co-Advisor.
Each Graduate Minor student is associated with the Minor Advisor and the Graduate Committee
of the Department and is advised as needed.
Assessment
The vast majority of students who have completed the M.S. program transition into jobs in
industry; these graduates have successfully found positions in a variety of companies around
the U.S. and even overseas. Currently, as a sample, graduates are working in the following
companies: Deloitte (San Diego, CA), Knowledge Advisors (Chicago, IL), Dell (Round Rock,
TX), Intel (Portland, OR), Microsoft (Redmond, WA), JDS Uniphase (Germantown, MD),
Manhattan Associates (Atlanta, GA), Epic Systems (Madison, WI), USAA, Echostar (Denver,
CO), ProTrans International (Indianapolis, IN), Interactive Intelligence (Indianapolis, IN),
Gyansys (Indianapolis, IN), Sears (Chicago, IL), ChaCha (Indianapolis, IN), Angie’s List
(Indianapolis, IN), Northwestern University School of Medicine (Chicago, IL), Ratuken (Tokyo,
Japan), Nimbula (Santa Clara, CA), and Barclays (Pune, India).
A large population of the Department’s M.S. students is international. This trend, although adds
significant diversity to the M.S. program, needs to be balanced by admitting many domestic
students. The Graduate Committee has proposed “an introduction to Graduate Study” event
targeted at the current undergraduate students especially the ones that are in the Junior-level.
Generally, a few M.S. graduates have transitioned to Doctoral programs at other institutes,
though in recent years several M.S. students have been accepted into the Ph.D. program of the
Department. Last year, one M.S. student was admitted into the Ph.D. program at the University
of Utah. In addition, several M.S. graduates have successfully completed the IU MBA program,
and one student transitioned to the IU Law School after completion of his M.S degree.
In the Spring of 2011, the Department drafted a set of Student Learning Outcomes for Graduate
Programs. These outcomes include outcomes for both the Graduate Certificate and M.S.
programs, with additional outcomes for M.S. Project/Thesis and Ph.D. students. The full list of
Graduate Student Learning Outcomes can be found in the Appendix. There has not been
significant assessment conducted thus far at the graduate level based on the SLOs. The
Department’s Graduate Committee is currently working on devising strategies for program
57
evaluation based on these SLOs and would like to solicit the Review Committee’s suggestions
in this area. There are no PULs currently for graduate programs, though the Department has
very recently begun an effort to create a set of PULs for graduate programs based loosely on
the undergraduate PULs; in this effort also, the Department would like to solicit the Review
Committee’s suggestions.
Work done by those in the M.S. project, M.S. thesis and Ph.D. tracks has in many cases
resulted in co-authored publications in peer-reviewed venues, which can be considered as a
form of “external assessment.” Publications which have been co-authored by graduate students
are marked with the * notation in the appendix.
In summary, the overall trend in the M.S. program enrollment is moving in a positive direction,
with some specific issues (e.g., a lack of more domestic and minority students) to be addressed.
The direct admissions (i.e., without any conditions) into the M.S. program and the total
enrollments have also increased over time. The percentage of accepted students (from the total
number of applications) into the M.S. program has decreased in past few years (from 78% in
Fall 2010 to 54% in Fall 2014), indicating the more selective nature of the admission and the
increased quality of the M.S. program. The size of the Ph.D. program has also increased from
2007 with better quality of students applying to the program. These positive trends need to be
preserved over next few years as the size of all the graduate programs is expected to increase.
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Chapter 8 Research
The Department, in addition to quality teaching, places a significant emphasis on research and
innovation. The faculty has been constantly active and successful in the research mission of the
Department. In this chapter, a brief synopsis of the research activities is provided.
Research Overview
Abstracts Papers
(Journal and Conference)
Book Chapters
Presentations Colloquia
Total Active Awards
(internal and external, PI and Co-PI)
2007 0 53 6 16 5 8
2008 0 69 54 20 5 12
2009 1 77 15 30 9 13
2010 3 75 13 24 15 19
2011 2 81 49 27 17 29
2012 5 57 10 38 20 22
2013 4 63 15 32 17 29
Table 8.1: Research Productivity of the Faculty
External Funding
# Grants Proposed*
$ Grants Proposed*
# Grants Received*
$ Grants Received*
2007 13 $1,868,851 4 $457,131
2008 20 $1,958,869 4 $261,098
2009 42 $6,843,659 4 $347,937
2010 52 $9,047,130 9 $815,434
2011 36 $4,659,856 18 $935,213
2012 45 $5,285,283 9 $666,732
2013 28 $4,039,315 17 $1,257,980
Table 8.2: External Research Funding
*Table not all-inclusive; only includes grants where departmental faculty are PIs. External grants
only. Multi-year grants (such as CAREER awards) are included in the calculations as reported
in the university financial/grant system, where large awards are broken down by individual
award years, rather than have the total award amount listed in the award’s 1st year. Thus for
large awards that extend past 2013, the full amount of the award will not yet be reflected in this
table.
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As seen from the above tables, the research productivity (publications and grants) of the faculty
has also been on an upward swing. This is certainly a significant achievement considering the
relatively young age of the Ph.D. program and the current economic climate.
Faculty Research Groups
Since early 2000, The Department made a strategic decision of focusing its research efforts in a
few critical areas. These areas are: a) Database, Data Mining, and Machine Learning, b)
Software Engineering, Distributed and Parallel Computing, c) Imaging and Visualization, d)
Networking and Security, and e) Educational Research. Hence, there are five active research
groups, one each corresponding to the abovementioned areas, in the Department. A brief
description of the ongoing projects pursued in these groups is provided below. An inclusive
grant and publication list is included in the Appendices.
Database, Data Mining and Machine Learning (DDMML) Group
Faculty: Murat Dundar, Mohammad Al Hasan, Snehasis Mukhopadhyay, Yuni Xia
Collaborative Units: eBay Research Labs (Nish Parikh, Neel Sundaresan), Joe Bidwell (IU
School of Medicine), Oregon State University (Meghana Babbar-Sebens), Mathew Palakal
(IUPUI School of Informatics), Keith Dunker (IU School of Medicine), John Lee (IUPUI School of
Engineering), Bartek Rajwa and Paul Robinson (Bindley Bioscience Center, Purdue University),
Sunil Badve (IU School of Medicine), Metin Gurcan (The Ohio State University School of
Medicine)
Understanding how human brain or other intelligent biological systems function has always
been one of the holy grails of human knowledge endeavor. Recent advances in learning and
adaptive systems make this goal less remote and unattainable than before. New computing
paradigms such as artificial neural networks, machine learning, and multi-agent systems
incorporate the ability to solve complex problems and make decisions in a dynamic environment
in an adaptive fashion. At the same time, the enormous growth of computational power, cheap
storage, superior communication networks, and sophisticated sensor devices has granted
scientists, government organizations, and enterprises an unprecedented ability to collect, and
store massive amount of data. Besides, the proliferation of handheld devices, and widespread
penetration of various online social networks, along with the availability of many cloud storages
have transformed numerous individuals from a data consumer to a data generator. These
phenomena have created a data-rich society where analyzing and mining data has become a
routine task. However, the availability of massive data has brought unique challenges both to
the data management task and also to the data analysis, mining, and learning task. The
DDMML group is dedicating its research to address these challenges. It develops theory,
systems, and applications for storing and managing of big data, mining data for knowledge
discovery, and building predictive models from dynamic and heterogeneous data. A brief
description of ongoing projects by DDMML is provided below.
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Interactive Pattern Mining from Massive Data (Mohammad Al Hasan and Snehasis
Mukhopadhyay)
One of the key bottlenecks of knowledge discovery through frequent pattern mining based
methods is the large size of the output which is caused by the combinatorial nature of the
pattern space. Due to this, it is difficult for the end users to select a succinct set of patterns for
utilizing them in subsequent tasks, such as classification, or clustering. In recent years, a large
number of works are attempted to address this limitation by mining patterns that are interesting
based on some additional metrics other than the frequency, however no universal metric exists
that works for all different application domains. So it is better to involve the end-user in an
interactive setup (aka, human-in-the-loop), where the user guides the pattern selection process.
The research objective of this project is to invent an interactive pattern mining method which
uses sampling based approach. In this paradigm, the sampler returns a pattern to the user who
provides feedback on that pattern, and based on the feedback the sampler updates the
sampling distribution so that subsequent patterns align well with user's interest. To assist the
user, the interactive system also equips the user with a visual interface, which further helps the
user to run visual analytics over the mined patterns. This project is funded by National Science
Foundation.
Scalable Methods for graph mining using MCMC Sampling (Mohammad Al Hasan)
The phenomenal growth of social, communication, and information networks over the last
decade has motivated the scientists to work on various interesting problems relating to graphs,
including, mine interesting subgraphs, model the temporal growth of social networks, model the
diffusion of nodal attributes over the networks, predict the status of a node or a link, and
discover the community structure in a network. However, given the gigantic size of today's
networks, one of the foremost challenges for each of the above tasks is the lack of scalability of
the existing algorithms for mining and learning on graphs. The high computation cost of typical
graph algorithms are to blame for this limitation; subgraph isomorphism is NP-Complete; graph
isomorphism is costly with an unknown complexity status; even polynomial tasks, such as
finding eigenvalues of a graph Laplacian, has roughly cubic complexity, and they are deemed
infeasible for networks with millions of vertices, and edges.
The research task of this project is to discover sampling based strategies for obtaining scalable
solutions for mining, modeling, and analyzing large graphs. The core idea of this direction is to
perform a random walk over the candidate pattern space and sample patterns using Markov
chain Monte Carlo (MCMC) method that maximizes some objective criteria. This project is
funded by the National Science Foundation.
Mining big-data using MapReduce based distributed system (Mohammad Al Hasan)
Since its inception almost a decade ago by Google engineers, the MapReduce has gained
enormous popularity because of its economic efficiency, and automated fault-tolerant capability.
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Now it has become a de-facto standard for running peta-scale analytics in the industry. The goal
of this project is to design distributed graph mining algorithms that run on MapReduce platform.
A parallel goal is to teach MapReduce based computation paradigm to new generation of
students. To carry out this project, the PI has already built a 10-node Hadoop cluster using
National Science Foundation funding. A new MapReduce based graph mining software is also
released recently.
Understanding the intent of e-Commerce Queries for better relevance matching
(Mohammad Al Hasan, in collaboration with eBay Research Labs)
The aim of this project is to understand the user’s search intent by understanding e-Commerce
queries. The project covers several tasks, which include computing query similarities for
substitution, query segmentation and understanding, query suggestion, and search result
ranking. The researchers at eBay research Labs are key partners in this project.
Development of Key Technologies for Big Data Analysis and Management Software
Based on Next Generation Memory(Yuni Xia, in collaboration with John Lee)
This collaborative project seeks to develop big-data main memory database management
system and distributed streaming processing system using hardware acceleration techniques
including FPGA ( programmable / reconfigurable chips) and GPGPU(general purpose graphics
processing unit).
Uncertain Data Management and Mining (Yuni Xia)
Due to limited bandwidth and battery power, it is infeasible for a system to keep track of the
actual values continuously produced by sensors. Database queries, in this case, may produce
incorrect answers. This project models the uncertainty inherent to dynamic sensor data with a
range of possible values together with the probability distribution of the values within that range.
Probabilistic queries, which evaluate uncertain data and produce answers with probabilistic
guarantees, are proposed and their efficiency issues are evaluated. In particular, the project
proposes efficient algorithms to enhance the performance of nearest-neighbor queries, range
queries, and joins. It has also developed uncertain data classification, clustering and association
mining algorithms.
DisProt Database: A Central Repository of Information on Intrinsically Disordered
Proteins( Yuni Xia,in collaboration with Keith Dunker)
The goal of this project is to fully develop DisProt, a database that provides an essential
depository of information about intrinsically disordered proteins (IDPs). DisProt will be not only
a collection of data on intrinsically disordered proteins and their functions, but also a unique
research tool to conduct various computational studies on these proteins and to help design
better research strategies for studying individual IDPs in laboratory. It's expected that DisProt
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will support a very wide-spread use, both for the purpose of carrying out bioinformatics
experiments and for the entire community involved in understanding cell and molecular biology.
Large Scale Sensor Stream Analysis and Mining for Geriatric Care (Yuni Xia, in
collaboration with IBM Watson Research Center)
This project aims to design and develop a real-time distributed sensor stream monitoring and
analysis system for geriatric care. This enables effective home-based continuous geriatrics
care, which is not only cost-savings, but also improves the quality of life of the elderly and their
families.
TrafficAnalyzer: A Real-time Traffic Stream Processing and Analyzing System(Yuni Xia,
in collaboration with IBM Watson Research Center)
Modern traffic monitoring systems are required to perform real-time processing and analysis of
peta-bit continuous data streams. This project proposes to design and develop a real-time traffic
stream processing and analyzing system. The most important feature of TrafficAnalyzer is the
real-time performance. The results of processing need to be produced with virtually zero
latency, because in traffic monitoring system, real-time response is crucial for reducing accident
rate and smoothing traffic flow. TrafficAnalyzer must support sophisticated time-windowed
processing operations since streaming data continually changes, often at high rates. These
operations should be executed in a way that produces results incrementally as new data arrives,
since the entire data set is never available in its entirety. TrafficAnalyzer also provides careful
management of the historical data, as it need compare and combine present data with the past
to study the traffic flow change over the time. TrafficAnalyzer is also resilient to inaccuracy and
uncertainties in the data streams, because inherent variations, losses, or reordering of the data
streams cause data to arrive in the wrong order, or with variable delays.
Development of SYMBIOTE; A Reconfigurable Logic Assisted Data Stream Management
System for Multimedia Sensor Networks (Yuni Xia, in collaboration with John Lee)
Numerous emerging applications require real-time processing of high bandwidth multimedia
data streams. This project proposes a novel class of data stream management systems called
Reconfigurable Logic Assisted DSMS (RLADSMS) that will provide one of the first
comprehensive and demonstrative approaches to using Reconfigurable Logic coprocessors as
data stream accelerators in the prototype RLADSMS called SYMBIOTE. This project will
investigate key issues such as data models, query languages, hardware DSMS operators,
corresponding cost models of query execution, considering hardware complexity of database
operators, run-time complexity of hardware and software operators, interconnect latencies,
bandwidth, resource allocation as well as optimization techniques for this new class of data
stream management system.
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WRESTORE: Watershed REstoration using Spatio-Temporal Optimization of REsources
(Snehasis Mukhopadhyay, in collaboration with Meghna Babbar-Sebens, Oregon State
University)
WRESTORE (wrestore.iupui.edu) is a web-based, user-friendly, interactive, transparent, and
participatory decision support system for helping land owners, government agencies, policy
makers, planners, and other stakeholders design a distributed system of conservation practices
in their watersheds. In WRESTORE, web-based tools allow users to test multiple solutions (or,
alternatives) for locating and designing conservation practices in a simulated environment of
their watershed landscape. Based on the overall performance of the practices in the simulated
environment, users can then identify alternatives most suitable to their needs. The interactive
framework takes feedbacks from the users and then uses an iterative search-and-learning
method to search for better potential solutions that incorporate the users’ feedbacks. In this
manner, not only does the tool learn from the users about their needs while searching for better
alternatives, the users also learn about how their watershed can respond to various actions or
changes on the landscape and how to manage their landscape within the constraints of their
physical and socio-economic environment. For example, a farmer concerned with the problem
of erosion on her/his land can explore multiple types of best management practices and
locations where the practices can be implemented on her/his landscape.
Fast Reinforcement Learning using Multiple Models and State Decomposition (Snehasis
Mukhopadhyay, in collaboration with K. S. Narendra, Yale University)
Originally based on mathematical psychology models of animal and child learning,
reinforcement learning algorithms aim to find the optimal decisions (or, decision rules) in
feedback with an unknown and uncertain teacher or environment, on the basis of a qualitative
and noisy on-line performance feedback provided by the teacher in the form of a reinforcement
signal. One of the well-known and major limitations of current reinforcement learning methods
proposed in the literature is the slow speed of convergence. This slowness, in turn, may be
attributed to the underlying assumption that the identification model of the environment needs to
be learned from complete ignorance using a single model, and also to the well-known “curse of
dimensionality” associated with a high dimensional state space.
The principal objective of this project is to address this important problem using the following
novel methods:
i. the use of multiple identification models.
ii. decomposition of high dimensional state and action spaces.
Self-adjusting Models as a New Direction in Machine Learning (Murat Dundar
Traditional supervised learning algorithms assume that the list of classes defined by a training
data set is exhaustive and that new data samples originate from one of the existing classes
represented in the training data set. This assumption is not very realistic in many real-world
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domains as the data-generating mechanisms constantly evolve and new classes of interest
emerge on a continual basis. Under such circumstances it is impractical if not impossible to
define a training data set with a complete set of classes. When the training data set is not
exhaustively defined, a future sample of a class not represented in the training data set will be
misclassified with certainty, leading to an ill-defined classification problem. This study offers a
new direction for supervised learning that relaxes the fixed-model assumption defined by the
existing data in order to have a self-adjusting model that can evolve by dynamically adding new
classes to better accommodate prospective data in offline as well as online settings.
Specifically, the aims of the project include (1) studying non-parametric prior models to
dynamically model the number of classes (2) developing new online and offline inference
techniques in partially-observed settings (3) modeling the rapidly accumulating nature of
samples evident with emerging classes (4) automatically associating a newly discovered class
with higher-level groups of classes in an attempt to identify potentially interesting class
formations, and (5) developing partially-observed tree models containing observed and
unobserved nodes, where observed nodes represent existing classes and unobserved nodes
are introduced online to fill the gaps in the existing data hierarchy that become evident only with
the arrival of new data.
Automated Spectral Data Transformations and Analysis Pipeline for High Throughput
Flow Cytometry (Murat Dundar, in collaboration with Bartek Rajwa and Alex Pothen,
Purdue University)
High-throughput flow cytometry is an emerging cell-analysis and screening technique employed
in various fields of life-sciences, including drug discovery and clinical research. One of the major
limitations of HT-FC is the lack of robust, rapid, and reproducible tools for data analysis and
data mining. The current paradigm of FC analysis does not fit suit the HT format well.
Traditionally, FC data are analyzed employing interactive exploratory visualization, which
requires preparing a number of 2-D scatter plots that are used by an FC operator or researcher
for visual evaluation of sample characteristics. Although the recent interest of computer science
and bioinformatics communities in FC has spurred development of automated compensation
and gating techniques, the proposed algorithms still follow the traditional analysis pathway
(compensation plus gating), and typically attempt to mimic trained human operators in
delineating various cell populations defined by the presence of fluorescent markers of varying
intensities. Unfortunately, this model is not sustainable when hundreds or thousands of data
sets must be processed in real time. This proposed research attempts to radically re-invent the
FC data analysis pipeline for high-throughput FC by employing spectral classification
approaches to FC data. In the proposed framework the FC data will be modeled as a mixture of
signals that can be quantitatively recovered if certain physical and biological constraints
describing the experimental system are rigorously followed. This project proposes a set of
algorithms that will allow first to define and encode the domain knowledge describing the
analyzed specimens, subsequently to approximate the concentrations of labels, and from there
recover information about the presence or absence of specific phenotypes of interest. The
techniques employed will functionally replace two steps in FC data analysis that have
traditionally been viewed as separate: compensation and gating. Instead, a new iterative
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spectral classification process will recover the quantitative characteristc of samples. This will
allow for fast and automated extraction of sample features, as well as for mining the collected
specimens for similar datasets. The proposed algorithm will be prototyped using R language for
statistical computing, and relevant procedures will be made available to other researchers in the
field of FC via the Bioconductor project. Upon successful testing and validation using various
datasets contributed by collaborators, the classification algorithms will be implemented in
PlateAnalyzer, an HT-FC data analysis package developed at Purdue University.
Machine-learning Approach to Label-free Detection of New Bacterial Pathogens (Murat
Dundar, in collaboration with Bartek Rajwa, Purdue University)
Technologies for rapid detection and classification of bacterial pathogens are crucial for
securing the food supply. A light-scattering sensor recently developed for real-time detection
and identification of colonies of multiple pathogens has shown great promise for distinguishing
bacteria cultures at the genus and species level for Listeria, Staphylococcus, Salmonella, Vibrio,
and Escherichia Coli. Unlike traditional testing methods, this new technology does not require a
labeling reagent or biochemical processing. The classification approach currently used with this
technology relies on supervised learning. For an accurate detection and classification of
bacterial pathogens, the training library used to train the classifier should consist of samples of
all possible forms of the pathogens. Construction of such a training library is impractical if not
impossible due to the high mutation rate that characterizes some of the infectious agents. This
project proposes to advance this sensor technology to allow for the detection of new
classes/subclasses of bacteria, which do not exist in the training library. Learning with a non-
exhaustive training library is an ill-defined problem. A two stage classification scheme to
alleviate this problem is designed. The first stage, i.e. detection, identifies whether the bacteria
sample belongs to one of the subclasses in the training library or a yet unseen and thus
unrepresented subclass. If the former is true, the sample is fed to the second stage, i.e.
classification, where it is classified to one of the existing subclasses. If the latter is true, an alert
is raised and the sample is saved for follow-up analysis.
Software Engineering, Distributed and Parallel Computing (SEDPC) Group
Faculty: Ray Chin (Emeritus), James Hill, Andrew Olson (Emeritus), Rajeev Raje, Fengguang
Song, Mihran Tuceryan
Collaborative Units: University of Alabama at Birmingham and University of North Texas
(Barrett Bryant), Naval Postgraduate School (Mikhail Auguston), Oregon State University
(Meghana Babbar-Sebens), Vanderbilt University (Annirudha Gokhale, Douglas Schmidt),
University of Maryland (Adam Porter), University of Adelaide (Katrina Falkner), University of
Tennessee (Jack Dongarra)
The SEDPC group is investigating various fundamental and applied research issues that arise
in creating high-performance, distributed, pervasive, and quality-aware software systems. Such
systems are omni-present in today’s world and span a large spectrum of application domains
from scientific and engineering to defense. As appealing the applicability of these systems is,
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their software realization is hardly a trivial task due to challenges such as the complexities and
scale associated with the domain, the performance and quality of service (QoS) issues, the trust
metrics and effective validation techniques. The projects pursued by the SEDPC group aim to
address some of these challenges. A brief description of these projects is provided below.
System Execution Modeling Environment Research and Development: Phase 1 – 5
(James H. Hill)
This project is a multi-phased project. The significance of the first 2 phases was to showcase
that it is possible to generalize several system execution modeling capabilities, such as
modeling, system integration, and performance analysis, to many different middleware
technologies and execution environments. The last 3 phases of the project focused on building
an international collaboration effort between the Australian DSTO and the University of
Adelaide. Dr. Hill’s role in the collaboration is the focus on unclassified open research problems.
The University of Adelaide then focuses on adapting the solutions created to the needs to the
Australian DSTO. This project has been supported by the Australia Defense Science and
Technology Organization (DSTO)
Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phases 1, 2, and 3 (James
Hill and Rajeev Raje)
This project is focused on assessing the effectiveness of various Static Code Analysis (SCA)
tools. The techniques used in the assessment involve the use of Joliet test suite from the NIST
and principles of machine learning. The underlying hypothesis behind this study is that such an
analysis will allow the prediction of which static code analysis tool will perform best on a given
piece of source code (i.e., find the most number of flaws in source code while having a low false
positive rate). The initial results from this project have been integrated into the SoftWare
Assurance Marketplace (SWAMP). This will allow SWAMP’s end-users to evaluate the quality of
static code analysis tools. This work has also provided preliminary data for a white paper that
has been approved for a full proposal currently under review for the DHS Long Range BAA
(DHSST-LRBAA14-02). The proposed project is research collaboration between IUPUI and
University of Maryland, College Park. This project has been supported by the Lockheed-Martin,
Northrup Grumman, and the Department of Homeland Security via the Security and Software
Engineering Research Center.
Automatic Identification of Software Performance Anti-patterns in Cloud Computing
Applications (James Hill)
In this project, an approach for identifying software performance anti-patterns in cloud
computing applications was suggested and tested on the Amazon Cloud. This project has been
supported by Amazon Inc.
Continued Support for Research and Development on System Integration Testing as a
Service (James Hill)
This project was an extension awarded to Dr. Hill after completing the AFRL Summer Faculty
Program in Summer 2010. The project performed a feasibility study of using abstract models to
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define and generate performance and system integration tests for large-scale component-based
distributed systems. This end result of such an approach would alleviate distributed system
developers from having to re-write large amounts of code to execute performance and
integration test across different systems under development.
Cyber-physical multi-core Optimization for Resource & cachE effectS [C2ORES] (James
Hill)
The project developed a methodology for emulating CPU workloads that can easily adapt to
many core architectures (e.g., 2-core, 4-core, 16-core, 64-core) without requiring integrated
knowledge of the underlying architecture or manufacturer. This project was supported by the US
Office of Naval Research with the Vanderbilt University as the Primary organization.
EISA/OASIS Transition Project – Transition Planning, Phases 3 and 4
The aim of this project was to investigate integrating event-based middleware into real-time
instrumentation of software systems. This project was supported by the Science Applications
International Corporation with the Vanderbilt University as the Primary organization.
CoSMIC Extensions for the Scalable Node Architecture (James Hill)
The aim of this project was to extend the CoSMIC tool suite, which is maintained by Dr. Hill, to
support new functionalities of the DAnCE research artifact developed and maintained by the
Vanderbilt University. The project provided with real-life case studies that are used to train
students who are interested in model-driven engineering for component-based distributed
system development. This project was supported by the Northrup Grumman Corporation with
the Vanderbilt University as the Primary organization.
Reducing Accidental Complexities Associated with CoSMIC Tool Suite – Phases 1 and 2
(James Hill)
The aim of this project was to extend the CoSMIC modeling tool to support the new Interface
Definition Language (IDL) 3+ specification from the Object Management Group (OMG). The
methodology created as a result of these explorations for modeling connectors in IDL3+ was
adopted by Zeligsoft CX and Artisan Studio, which are commercial available tools for designing
component-based systems that use IDL3+. This project was supported by the Northrup
Grumman Corporation with the Vanderbilt University as the Primary organization.
Modeling, Specifying, Discovering, and Integrating Trust into Distributed Real-time and
Embedded (DRE) Systems – Phases 1 and 2 (Rajeev Raje and James Hill)
The aim of this project is to research and develop a methodology for integrating trust into each
phases of the software lifecycle. The concept of trust in the context of individual software
components and their aggregated system was defined as the conformance of these entities to
their specifications. This definition was quantified as a tuple of belief, disbelief and uncertainty
using the theory of belief functions. This quantification was applied to both the inside (i.e., a
developer’s) and outside (i.e., the user’s) views of that entity. Various different operators for
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selecting, composing and predicting trust values of combinations of components were
suggested and applied using the topology of communication the association between different
entities and machine learning principles. The outcomes of these efforts have been applied to
publically available resources such as the Google’s Play Store, Microsoft’s Store and the QWS
dataset. This project has been supported in part by the Air Force Research Labs via the
Security and Software Engineering Research Center.
A Narrative-based Approach to Requirement Analysis (Rajeev Raje and James Hill)
Software requirements, which typically are written as free flowing text, for complex, and often
distributed, projects present myriad of challenges to stakeholders such as the domain experts,
designers, developers, testers, and user. The process of analyzing these requirements is
laborious and requires a large amount of manual intervention. This project suggests a different
alternative to the analysis of requirements based on the notion of a narrative-based analysis – a
technique that uses principles of NLP and AI – and has been successfully used in analyzing
folklore tales by MIT researchers. The outcome of this research will address these critical
questions: a) what abstractions are necessary to identify and extract underlying knowledge from
a sample set of requirements?, b) can such an analysis also highlight inconsistencies, if any, in
the requirements?, and c) can guidelines be created/inferred using this analysis to better
formulate requirements? Answers to these questions will help in achieving better insights into
the requirement analysis process. A proposal based on this is currently pending at the Security
and Software Engineering Research Center.
UniFrame – A Framework for Seamless Integration of Heterogeneous Distributed
Components (Rajeev Raje and Andrew Olson)
Software intensive distributed systems are omni-present in today's world. The realization of
such systems, mainly due to the reasons of economy and scalability, should be achieved as
loose coalitions of independently created components that offer services. This is an incarnation
of McIlroy's vision of a component bazaar in the context of distributed systems. At the same
time, the proliferation of these systems into a variety of domains, including critical applications,
is necessitating a high confidence about these individual services and an ensemble created out
of them. The creation of dependable software systems is so critical that it is listed as one of the
grand challenges in computing research. If future distributed systems, requiring a desired level
of high confidence, have to be composed out of independently created and deployed services,
then care must be taken in creating, specifying, deploying, discovering and evaluating
appropriate services for a specific instance of a distributed system, as the confidence of the
ensemble depends upon the confidence about individual services and their inter-dependencies.
This research is carried out under the umbrella of UniFrame project. A part of this project was a
collaborative venture between multiple universities and was supported by the US Office of Naval
Research and the Indigo Foundation between 2001 and 2006. Recent efforts have focused on
distributed discovery of resources and some of the results have been applied at searching
software services for the domain of Earth Sciences.
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A Framework for Development of Adaptive Software Services and Systems (Rajeev Raje)
Many applications demand for the development of software services and systems composed of
them to be adaptive in response to certain stimuli. However, the adaptive ability of a software
component is often considered as an after-thought instead of following the notion of “adaptive
by construction”. This project aims at formalizing the types of adaptations, creating their
taxonomy, providing programmatic representations of adaptations and a tool suite to develop
adaptive components from the start. The principles developed are being applied to publically
available software repositories such as the Google Play Store.
eDOTS - An Opportunistic Indoor Tracking Solution (Rajeev Raje, Mihran Tuceryan, and
Fengguang Song)
The eDOTS is an indoor tracking solution being currently developed at IUPUI. The goal of this
project is to make use of existing sensor networks and the proliferation of inexpensive and
available mobile sensor devices - opportunistic indoor tracking. Through this work a solution for
indoor tracking that prevents the need for installing a costly/invasive tracking infrastructure all
the while provide a high degree of accuracy in real-time is provided. The project uses the
principles of opportunistic discovery, sensor subset selection, data fusion, and trust and
reliability issues of sensors. This project would benefit many application domains including that
of health care and emergency rescue. Collaboration with the colleagues with the IU School of
Nursing is being investigated. This project is supported in part by the Purdue Research
Foundation and a proposal based on this effort is pending with the Samsung Corporation.
Mazu: A High Performance Computing Framework for Simulating Large-Scale
Computational Fluid-Structure-Interaction Dynamics (Fengguang Song)
The objective of this project is to design and develop a task-based domain-specific computing
framework to exploit parallelism, scalability, I/O optimization, and performance potential of
extreme-scale high performance computing systems to enable highly efficient Computational
Fluid Dynamics (CFD) computing. With a number of new and emerging disruptive technologies,
the conventional programming models used to develop CFD applications, such as MPI,
Pthreads, hybrid MPI/OpenMP, hybrid MPI/CUDA, are not well suited to scale on the new
heterogeneous extreme-scale HPC systems with billions of cores. In this project, a domain-
specific approach is taken to develop a fundamentally new task-based computing framework for
the CFD domain, called Mazu, to address the parallelism and scalability challenges presented
by extreme-scale HPC systems. This project involves creating and designing new scientific
libraries, programming models, runtime systems, computational fluid dynamics methods, and
optimization methodologies.
Building an Open-Workflow Scheduling Middleware for Water-Related Communities on
Cluster/Grids/Cloud (Fengguang Song)
The goal of this project is to build up an open and sustainable modeling infrastructure for
scientific water-science communities. A lightweight middleware is designed and implemented to
enable easy access and efficient large-scale computations on distributed computing resources.
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The project will provide as simple a user interface as possible and hide all the details of various
types of resources provided in the backend such as clusters, Grids, and Clouds. From an end
user’s perspective, his or her only task is to draw a workflow that couples different
computational models without knowing anything about parallel programming. The middleware
automatically finds and allocates high performance computing resources, schedules and
executes the user’s workflow dynamically. This project will have profound impacts in almost
every discipline in water science and beyond, where scientists can use this project to greatly
simplify their data access, model studies, coupling and HPC facility access, and thus boost their
research explorations.
A High Performance Synergistic Software System for both Compute-Intensive and Data-
Intensive Computing (Fengguang Song)
The goal of this project is to integrate and couple extreme-scale computing with IO-intensive big
data analysis to simplify, enhance, and speedup the scientific discovery process. In this project,
new computing technologies to manage/schedule large-volumes of data and large computation
simultaneously, to maximize data locality, minimize synchronous operations, design out-of-
memory algorithms, and to create simplified and flexible programming models to improve the
productivity of various domain scientists are being developed. A scalable and general
computing framework is being developed to co-schedule advanced computations, data
analyses, and I/O activities. This project is expected to fundamentally advance both scientific
computing and extreme-scale big data analytics.
Imaging and Visualization
Faculty: Shiaofen Fang, Mihran Tuceryan, Jiang yu Zheng, Gavriil Tsechpenakis, Li Shen
(adjunct)
Collaborative Units: Indiana University School of Medicine; Indiana University School of
Dentistry; Regenstrief Institute; Indiana Forensic Institute; Indiana University Advanced
Visualization Lab (AVL); Indiana University Center for Neuroimaging.
The Imaging and Visualization group’s research focus is on the processing, analysis, fusion,
and presentation of multi-modal, multi-dimensional and multi-scale sensory information using
visual computing techniques. The goal is to develop fundamental theory and strategies, and
efficient and effective algorithms and techniques for practical applications of important social
and scientific significance.
As part of the Imaging and Visualization group, the Center for Visual Information Sensing and
Computing (VISC) is a cross-disciplinary research center, dedicated to fundamental and applied
research in the emerging field of multi-sensory information processing. As the primary means in
which digital world interacts with the physical world, sensory technology (e.g. electronic
surveillance, 3D range data scanning, biometric sensory, medical imaging, forensic and law
enforcement imaging, remote sensing, senor networks) is rapidly becoming an integrated part of
everyday life. The science of understanding and reasoning with sensory information constitutes
the “brain” of a digital system, and is destined to become a grand challenge of the new century.
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Sensor systems provide digitized information of real world, thus allowing computers to interact
with the physical world in its digital forms. The most direct use of sensory information is
visualization in which virtual objects and digitized real objects are rendered together within the
same virtual environment. The landscape of sensory technology has, however, drastically
changed recently. On one hand, sensory systems have become much more sophisticated, less
expensive, more convenient, and faster. In particular, mobile platforms have become ubiquitous,
cheap, and portable with great computational power and a plethora of sensors that are built in.
This allows for new applications of imaging and visualization techniques to practical real-world
problems. Similar progress has also been made in remote sensing, satellite imagery, panoramic
imaging, video surveillance, and medical imaging. Ubiquitous and high quality and multi-
dimensional sensory data from multiple sensors of various scales allow the applications not only
to visualize but also to analyze and understand the physical world. Understanding sensory
information involves extracting or verifying meaningful information from raw sensory data.
Examples include shape features, identities, patterns, emotions, expressions, movements,
behaviors and relationships. Such sensory intelligence has the potential to revolutionize a wide
range of applications such as medical diagnosis, security screening, electronic surveillance,
scientific discovery, law enforcement, forensics, intelligent virtual environment, etc.
The specific research projects (current and past since 2007) of Imaging and Visualization group
are:
3D Facial Image Analysis for FAS Diagnosis (Shiaofen Fang, Li Shen).
This is a collaboration with Tatiana Foroud from IU School of Medicine and Rick Ward from
Anthropology. The project seeks to collect a longitudinal, multi-ethnic sample of individuals
prenatally exposed to alcohol. This sample will allow for a reliable separation of the effects of
ethnic variation and developmental age from those due to alcohol exposure. The focus is on
enhancing the understanding of FASD dysmorphology through 3D image analysis. Fetal Alcohol
Syndrome Disorder (FASD) is a neurological abnormality caused by exposure to alcohol during
pregnancy. Children with FAS disorder often exhibit characteristic facial features that can be
used in FAS diagnosis. As the Imaging Core of the NIH funded International Consortium for
FASD (CIFASD), novel 3D surface analysis techniques were developed, using laser scanned
facial images, for effective early FAS diagnosis and the discovery of new FAS features and their
biological relationships. Geometric and visual feature detection methods were used to identify
potential facial features which were then analyzed by pattern recognition and machine learning
techniques. This research has led to significant success in FAS diagnosis. (Funding: NIH-
NIAAA).
Mouse Model Neuro-Facial Dysmorphology (Shiaofen Fang):
This is a collaboration with Feng Zhou and Yun Liang from IU School of Medicine. This project
aims to define a mouse model for facial and brain phenotypes as a function of the dose and
stage of embryonic development of the alcohol exposure. New applications of 3D Micro-video-
imaging and Micro-computed tomography (Micro-CT) imaging of facial and underlying
bone/cartilage allow, for the first time, high resolution analysis of surface-to-bone/cartilage
craniofacial dysmorphology from fetal ages to young adulthood. 3D surface reconstruction
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techniques from volumetric images were developed to generate surface features for pattern
recognition and analysis. (Funding: NIH-NIAAA).
Health Care Data Visualization (Shiaofen Fang):
This is a collaboration with Shuan Grannis (Regenstrief Institute), Mathew Palakal (Informatics)
and Yuni Xia (CS). The objective of this project is to investigate better visualization approaches
to improve the usability of emerging large scale clinical data sets by developing a prototype
open-source visualization framework that are adaptive to any application use cases and
datasets. The approach includes (1) constructing an information-rich concept space as a
uniform visualization platform by developing a set of data and text mining toolkits to extract
concept terms and their relationships; (2) developing a suite of visualization tools that are
automatically applied based on the user selected visualization objectives in the concept space;
and (3) building a configurable visualization interface for the different use case needs. The
visualization framework will be tested on a large scale healthcare database that is currently
available at Regenstrief Institute. (Fundig: DoD - US Army).
Modeling the structure and dynamics of neuronal circuits in the Drosophila larvae using
image analytics (Gavriil Tsechpenakis)
The ability to adjust dynamically to attain stability in the face of widely ranging internal changes
and external insults is a feature observed commonly in natural systems. The human brain, for
example, has an amazing capacity to functionally recover from strokes that caused damages to
local neuronal circuitries. Despite their scientific and social values, little is known about the
principles of such highly adaptive systems. However, recent advances in imaging and
computational technologies are practically ripe for visualizing and processing the small insect
brain in its entirety, down to the level of individual synaptic connectivity.
The objective of this project is the image-based computational modeling of how synaptic
connectivity is established in vivo during brain development, a major question in neuroscience
today. This project estimates and patterns the complete morphology, connectivity properties and
structure dynamics of single neurons and neuronal circuits in the Drosophila larvae. It is
anticipated that this work will set the principle for large-scale study of more complex brains at
single-cell resolution, and for modeling adaptive responses of neuronal circuits to changes such
as aging, disease and injury.
This work is sponsored by NSF CAREER and ABI Innovation grants.
A Novel Retinal Imaging Approach to Diagnose Glaucoma (Gavriil Tsechpenakis)
Glaucoma, a leading cause of blindness worldwide, can be detected using retinal thicknesses
from spectral-domain optical coherence tomography (SD-OCT) scans of the macula. This
research calculates the desired thickness maps as the distance between the inner-limiting
membrane (ILM) and retinal pigmented epithelium (RPE) of the retina. Then, thickness map and
symmetry are calculated, using image-based criteria, rectangular grid (average thickness within
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each bin), and arcuate grid, considering the anatomy and orientation patterns of the nerve
fibers.
This work is sponsored by the Indiana University Collaborative Research Grant initiative.
Prediction and simulation of angiogenesis using growth factor delivering scaffolds
(Gavriil Tsechpenakis)
The development of organized vascular networks necessitates a tightly regulated interplay
between variable cells, growth factors and soluble mediators. The long-term goal of this project
is to develop therapeutic angiogenic strategies based on the rational design of cytokine
releasing constructs that promote vascular patterning and vessel stability. The objective of this
work is to (i) develop electrospun, three-dimensional constructs with patterned architecture, (ii)
demonstrate that the spatial and temporal delivery of two model angiogenic growth factors
promotes the formation of an organized capillary network and (iii) develop a computational
model that can predict the biological effect of a growth factor releasing construct as a function of
specified fabrication parameters. It is hypothesized that guided therapeutic angiogenesis (i.e.
patterned vascular networks) can be obtained by controlling the spatial and temporal
presentation of soluble mediators at the site of ischemia.
This work was sponsored by a NIH R21 grant subcontract from the University of Miami.
In situ Protein-protein interaction networks (Gavriil Tsechpenakis)
This is a collaborative project that redefines the proteomics as a context-rich molecular
bioinformatics. Proteomics has been hailed as 'the next big thing' after genomics. It has
progressed from cataloging the whole complement of proteins, or proteome, to charting their
interactions, or interactome. Yet the major predicament in proteomics today is its paucity of in
situ contexts. The cross-disciplinary team (University of Miami and IUPUI) launched an imaging-
based survey of protein-protein interaction networks within neurons. Its ultimate goal is
reconstruction of genome-wide protein-protein interaction networks within each and every
subcellular compartment of neurons at progressive steps of their development. This project is
the first systematic inspection of when and where each protein-protein interaction takes place in
vivo. The investigators bring their expertise in neuronal imaging, Drosophila genetics, and
computational analysis.
Over one million 3D images of model neurons were analyzed to construct proteomic maps
specific for different neuronal types, developmental stages and subcellular compartments. This
image library underwent cross-correlation analysis to arrive at a model of the dynamics of the
molecular networks of wild type neurons. At its completion, the project not only delivered the
first context-rich proteomics resource, but also offered a new intellectual infrastructure for
determining the molecular circuitries affected by neurological disorders, aging or drug addiction
and designing strategies to repair and/or protect neurons.
This work was sponsored by a NIH RC2 grant subcontract from the University of Miami.
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Forensic Identification using Bitemarks (Mihran Tuceryan in collaboration with Herbert
Blitzer of the Indiana Forensic Institute in Indianapolis)
Forensic odontology generally addresses the problem of identifying individuals based on the
properties of teeth or identifying individuals based on bite mark impressions left behind in crime
scenes. This project explores methods of matching 2D bite mark images to 3D models (made
from dental casts) of suspects. The specific goal of the project is to develop a population model
of the probability of match using a collection of such dental casts.
This project was funded by the National Institute of Justice (NIJ).
Advanced In-car Video System (Mihran Tuceryan, Jiang Yu Zheng, in collaboration with
Herbert Blitzer of the Indiana Forensic Institute in Indianapolis)
This work aims at real-time in-car video analysis to detect several critical events during a routine
traffic stop in order to alarm and assist police action. Particularly, detecting a tracked or stopped
vehicle is a crucial task for further examination of suspects, protecting police safety, and remote
monitoring from police station. Some examples of critical events are person running out of the
stopped car, package thrown out of the car during pursuit, door of stopped car opens, and
officer down.
This work employs a comprehensive approach to localize target vehicles in the video under
various environments and illumination conditions. The extracted geometry features on the
moving objects and background are dynamically projected onto a 1D profile and are constantly
tracked. Temporal information of features was relied upon for vehicle identification, which
compensates for the complexity of vehicle shapes, colors and types. The project investigated
videos of day and night, and different types of roads, proving that the employed approach is
robust and effective.
This project was funded by the National Institute of Justice (NIJ).
A Device to Digitize and Produce 3D Images of Impression Evidence (Mihran Tuceryan,
Jiang Yu Zheng)
In crime scene investigations it is necessary to capture images of impression evidence such as
tire track or shoe impressions. Currently, such evidence is captured by taking two-dimensional
(2D) color photographs or making a physical cast of the impression in order to capture the
three-dimensional (3D) structure of the information. In the case of tire track impressions that
have long spans, the current practice is to take multiple overlapping photographs and stitch
them together. The 2D photographs, under the right illumination conditions, may highlight
feature details in the evidence, but do not provide metric depth measurement information for
such features. Obtaining a 3D physical cast of the impression may destroy the evidence in the
process. Therefore, the use of a 3D imaging device which can capture the fine details of such
impression evidence without destroying the evidence can be a useful addition to the toolkit of
the crime scene investigators (CSI).
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This project designed such an impression imaging device and built a prototype hardware and
software which included a calibration method for obtaining the 3D image with the proper metric
information. The device can provide a depth resolution of around 0.5mm at the shortest zoom
setting and even higher resolution at longer zoom settings as well as a high resolution color
image properly registered with the depth image. It can digitize impression evidence which have
long spatial spans such as long tire track impressions which would be difficult to fit into a single
photographic shot. The device is portable, light-weight, and can be used outdoors. Even though
the main goal for the device is for use in forensic evidence collection, it can be used in other
application domains as well such as archeology, etc.
This project was funded by the National Institute of Justice (NIJ).
Sensor Network over 2D Communication LAN Sheet (Jiang Yu Zheng)
This is a cooperative project with National Institute of Information and Communication
Technologies in Japan. This project has designed and developed a media infrastructure that
can distribute multimedia signals and power via a two dimensional communication sheet. Small
and low power devices placed on top of the sheet will be able to receive audio and video signals
transmitted from a computer. The 2D sheet is printed in three layers and the electromagnetic
waves are distributed in the middle layer. Through a grid of slits printed on the top layer, the
electromagnetic waves leak and are thus picked up by small multimedia devices with antennas.
Compared to the wired and wireless communication, this 2D sheet has its unique properties
such as free to put and move devices on it, power efficient, wide transmission bandwidth,
secure communication, etc.
This project was funded by National Institute of Information and Communication Technologies
(NICT) in Japan.
Scenario Generation for Vehicle Testing (Jiang Yu Zheng)
This is a cooperative project with Yaobin Chen, Stanley Chien, and Lauren Christopher in the
Transportation Active Safety Institute in IUPUI. Extensive research interest from both vehicle
manufacturers and road safety practitioners has been focused on protecting vulnerable road
users such as pedestrians, bicyclists, and wheelchairs. Pre-collision systems (PCS), with
vulnerable road user detection capability are becoming a standard feature of active safety
systems in the market. Understanding the road user (pedestrians, bicyclists) behavior is
important to the pre-collision system design and testing. Large scale naturalistic driving data
analysis can provide valuable and objective information on how road users behave in real life.
Analyzing road user behavior within large scale naturalistic driving data requires efficient
detection methods. In this work, monocular based bicyclist detection in naturalistic driving video
is a very challenging problem due to the high variance of the bicyclist appearance and complex
background of naturalistic driving environment. In this paper, a two-stage multi-modal bicyclist
detection scheme was proposed to efficiently detect bicyclists with varied poses for further
behavior analysis. A new motion based region of interest (ROI) detection is first applied to the
entire video to refine the region for sliding-window detection. Then an efficient integral feature
based detector is applied to quickly filter out the negative windows. Finally, the remaining
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candidate windows are encoded and tested by three pre-learned pose-specific detectors. The
experimental results on the TASI 110 car naturalistic driving dataset show the effectiveness and
efficiency of the proposed method.
This project was funded by Toyota.
Video Profiling and Indexing in Large Video Websites (Jiang Yu Zheng)
Massive amounts of videos are uploaded onto video websites; with these videos smooth and
efficient video browsing, editing, retrieval, and summarization are needed. Most of the videos
employ several types of camera operations for expanding field of view, emphasizing events, and
expressing cinematic effects. To digest heterogeneous videos in video websites and databases,
video clips are profiled to 2D image scrolls containing both spatial and temporal information for
video preview. The video profile is visually continuous, compact, scalable, and indexed to each
frame.
This work analyzes the camera kinematics including zoom, translation, and rotation, and it
categorizes camera actions as their combinations. An automatic video summarization
framework is proposed and developed. After conventional video clip segmentation and video
segmentation for smooth camera operations, the global flow field under all camera actions has
been investigated for profiling various types of video. A new algorithm has been designed to
extract the major flow direction and convergence factor using condensed images. Then this
work proposes a uniform scheme to segment video clips and sections, sample video volume
across the major flow, compute flow convergence factor, in order to obtain an intrinsic scene
space less influenced by the camera ego-motion. The motion blur technique has also been used
to render dynamic targets in the profile. The resulting profile of video can be displayed in a video
track to guide the access to video frames, help video editing, and facilitate the applications such
as surveillance, visual archiving of environment, video retrieval, and online video preview.
SPHARM Shape Modeling and Analysis Toolkit for Brain Imaging (Li Shen, Shiaofen Fang)
Shape analysis is becoming of increasing interest to the neuroimaging community because of
its potential to provide important information beyond simple volume measurements and to
understand morphometric changes in neuroanatomical structures related to specific brain
disorders. The purpose of this project is to develop and release SPHARM-MAT, a 3D shape
modeling and analysis toolkit for neuroanatomical studies. SPHARM-MAT is a synergistic effort
in relation to existing tools, and is a powerful toolkit with several new features that add value,
including ease of use, broad applicability, good interoperability, and wide dissemination.
SPHARM-MAT is now available at http://www.nitrc.org/projects/spharm-mat, and has been
applied to several biomedical imaging applications.
This project was funded by National Institute of Health (NIH-NIBIB)
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Data Mining Framework for Genome-Wide Mapping of Multi-Modal Phenotypic
Biomarkers and Outcome Prediction (Li Shen, Shiaofen Fang)
Today's massive generation of digital data is greatly outpacing the development of
computational methods and tools and presents critical challenges for achieving the full
transformative potential of these data. This project employs the new capabilities of large-scale
data mining techniques in multi-view learning, multi-task learning, and robust classification to
address critical challenges in systematically analyzing massive multi-modal genetic, imaging,
and other biomarker data. Several sparse regression and classification methods have been
developed, and applied to the integrative analysis of imaging, biomarker and genetics data in
the study of Alzheimer’s disease.
This project was funded by the National Science Foundation (NSF-IIS).
Networking and Security Group
Faculty: Arjan Durresi, Yao Liang, Xukai Zou, Erman Ayday (to join in January 2015)
Collaborative Units: Purdue University (Elisa Bertino), Washington University in St. Louis (Raj
Jain), Fukuoka Institute of Technology Japan (Leonard Barolli), University of Central Arkanas
(Vamsi Paruchuri), QualComm (Eliza. Y. Du), School of Engineering and Technology, IUPUI (Feng
Li), School of Informatics, IUPUI (Jake Chen), Medical School, IU (Zeynep Salih), National Weather
Service (NWS), NOAA (Thomas E. Adams, Pedro Restrepo), USGS (Jerad Bales), CUAHSI,
(Richard Hooper), NASA (Steve Kempler, William Teng ), Univ. of Pittsburgh (Xu Liang).
Networking and security is a central fundamental part in today’s cyber technology. This research
includes Internet architecture, cyber infrastructure for sciences and engineering, wireless sensor
networks (WSNs) and Internet of Things, ad hoc mobile networks, Software Defined
Networking, networking and communication security, and various security and privacy issues in
real applications such as health care and personal genomics and electronic voting. Research
work and projects range from sensor network and data management, QoS over Software
Defined Networking, Internet mobility, sensor data compression, energy efficient WSN protocols
and deployments, WSN tomography, in-network processing, to trusted collaborative computing,
trusted social networking, biometrics and user authentication, secure online electronic
voting, genomic data security and privacy, and secure moving cloud and mobile computing
security.
Economically Viable Support for Network Mobility (Arjan Durresi)
Mobility is one of the top requirements for the future Internet. Recent studies predict that mobile
data traffic will continue to increase. Such technological changes are accompanied by new
types of interactions, including various forms of social networking applications, such as
Facebook and Twitter, which indicate the trend of more direct interactions among users. On the
other hand service delivery is being consolidated and empowered by various cloud computing
platforms and many such new applications need mobility support. This project proposes to
develop a Mobility Support Service (MSS) that will manage the mobility for its customers. MSS
will be offered by service providers dedicated to mobility, called Mobility Service Provider
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(MSP). MSP role could be played by existing network or content service providers too. MSS will
be offered as a value-added service to users who are willing to pay for it. Therefore, mobility
support will generate its own revenue and this will justify the business and investments of
MSPs. MSS does not require any change on access networks, existing network infrastructure,
legacy applications, and operating systems. MSS is a distributed service over the Internet, in
various implementation architectures, such as on premise, on cloud or hybrid at various degrees
between on premise and cloud.
Trust Management Framework for Social Networks (Arjan Durresi)
Social networking is one of the major developments of recent times. Very successful examples
of social networking include Facebook, Twitter, etc. Many other systems have components of
social networks, for example eBay, Amazon, and Epinions are based on user reviews. Humans
have built tools during history to enhance their capabilities. Computers are the signature tools of
the current time, and social networks enhance an individual’s social capabilities. All social
interactions are based on trust. Therefore, social networking will rely heavily on trust.
Conceptually, trust is also attributable to relationships within and between social groups
(families, friends, communities, organizations, companies, nations, etc.) It is then necessary to
develop tools that project trust capability to social networks. Furthermore, similar to real life,
such trust enhancement tools can enable security and trustworthy decisions in social networks.
In addition, this trust framework can be integrated in various social network analysis tools, by
increasing their trustworthiness. Finally, real life social networks are extremely large, therefore it
is necessary to use scalable graph based techniques in order to make trust solutions applicable
to real world social networks.
The approach in this project is based on the similarities between human trust operations and
physical measurements. They both are evaluations of some values, enhanced by repetition of
the evaluation. Furthermore, the uncertainty in measurement is similar to human confidence
when people make trust judgments. In addition, trusts are aggregated it becomes necessary to
take into account the corresponding confidence, similar to the theory of error propagation, in
which single step errors in a chain of measurements are aggregated. Therefore, this project is
working toward a general trust management framework including trust metrics and management
methods to aggregate trust, which are based on measurement theory and guided by psychology
and common sense. Furthermore, it explores the development of security mechanisms, based
on the proposed trust framework, against Denial of Service and cliques in social networks.
Video Over Software Defined Networking (Arjan Durresi)
Software-Defined Networking (SDN) is a new approach in designing and developing computer
networks. SDN, using similar concepts as seen in server virtualization, allows computer
networks to support the rapid changing business needs. The key concepts in SDN are
abstraction (i.e., network as a graph), network virtualization, automation and orchestration of
network services. These key concepts allow network services to be rapidly developed and
deployed. The control and data planes in SDN architecture are separated; the network
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intelligence and state is relocated in a centralized SDN controller. The network applications
(e.g., traffic engineering, network virtualization and path resiliency) are abstracted from the
network and relocated in the centralized controller. The SDN controller views the network
infrastructure (i.e., switches, routers, links and middle-boxes) as a graph; this is the core of SDN
architecture. The SDN architecture exposes the network infrastructure switch’s flow-tables with
an open programmable interface (e.g., OpenFlow or ForCES) which is used to program the
network. Video over Software Defined Networking (VSDN) architecture was developed to
address rigidity of the path selection process of today’s network architectures. VSDN provides
end-to-end QoS guarantees for video applications or other real-time applications. VSDN uses
the network global view to select the optimum path for video applications in terms of bandwidth,
delay and jitter. VSDN utilizes SDN architecture and OpenFlow protocol to separate the control
and data planes. The VSDN controller contains the routing logic and path selection application.
The core component of VSDN architecture is the routing module (RM) which performs the path
computation.
Open Application Delivery Networking Architecture (Arjan Durresi)
Social networking, personalized searches, recommendation based online shopping, online
banking, and other similar personalized services form the bulk of the traffic over the Internet
today. Cloud computing provides unique opportunities for these Application Service Providers
(ASPs) to manage and optimize their distributed computing resources. For this trend to be
successful, similar facilities need to be developed for on-demand optimization of connectivity
resources to enhance user experience. The usual solution of content distribution networks (e.g.,
Akamai) is not always feasible due to the sensitivity of the personalized data and also because
such data is mostly dynamic and hence static caching is not feasible. Many big ASPs, such as
Google and Microsoft, have solved this problem by having their own WAN optimized and
customized for delivery of their applications. This project works to develop an open application
delivery network architecture (OpenADN) which allows any ASP to be able to optimize their user
experience using a shared public WAN infrastructure in a multi-cloud environment, that is,
multiple cloud computing facilities belonging to multiple cloud providers and private data
centers. Application delivery is different from content delivery in that it allows ASPs to deploy
customized dynamic virtual network topologies and achieve replication, load balancing, server
mobility, fault tolerance, and end-to-end security. OpenADN extends software defined
networking (SDN) which provides new opportunities for designing control architectures for
networks by providing cleaner abstractions between the network control and data planes. These
extensions include several innovative techniques including control plane programmability using
rule-based delegation, cross-layer communication, context routing, control/data plane
separation, id/locator split, and application level flow-based routing to provide these services to
ASPs. Control plane programming using a Rule-based delegation allows an ASP to customize
and optimize network handling of their application traffic. These delegation rules may include
rules for how to select the instances among multiple replicas of the server, how to load-balance
among different instances, what to do under network and instance failure (due to security attack
or hardware malfunction), forward application traffic over off-path middleboxes, etc. The internet
service providers (ISPs) or cloud service providers (CSPs) can then translate these rules into
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forwarding rules for the data plane, using data-plane programmability as an extension of
OpenFlow.
Secure Communications among Cell Phones and Sensors for Medical Applications
(Arjan Durresi)
Various healthcare areas such as diagnosis, surgery, intensive care and treatment, and patient
monitoring in general, would greatly benefit from light, autonomous devices which can be
unobtrusively mounted on the patient’s body in order to monitor and report health relevant
variables to an interconnection device in the vicinity. This interconnection device should be able
to connect to access points at different locations within the healthcare institution. Examples of
health sensors include: ECG (Electrocardiogram) for monitoring heart activities, EMG
(Electromyogram) for monitoring muscle activity, EEG (Electroencephalogram) for monitoring
brain electrical activity, SO2 sensors for measuring of oxygen level in the bloodstream, Blood
pressure monitoring sensor, Tilt sensor for monitoring trunk position, Breathing sensor for
monitoring respiration, Motion sensors for recording user’s status and level of activity. This
project uses sensor and other wireless devices in health care, teaching, evaluation and
research areas in resuscitation of the newborn in the neonatal intensive care unit and also is
investigating a new, secure and hybrid communication architecture among cell phones and
sensor networks to be used in medical applications. It is believed that cell phones could be used
to communicate aggregated sensed data to users in real time. The ubiquity of cell phones
makes them the ideal candidate to be used as user interface to sensor data. The features and
available resources of cell phones have been increasing at a staggering pace. Sensor
limitations make symmetric cryptography based schemes, such as key predistribution suitable
for sensor networks. However, in general, such schemes provide weak authentication and non-
repudiation. Stronger authentication with key predistribution can be achieved by using pairwise
keys. The use of cell phones as communicating devices with sensor networks has two major
advances from the secure view: (1) cell phones have the necessary resources to perform
asymmetric key cryptography, and (2) the cellular infrastructure can be used to obtain the
required pairwise keys to perform secure authentication.
Using Lessons from the Disaster in Japan to Develop Communications for Emergency
Situations (Arjan Durresi)
The goal of this project is twofold: First, it studied cell phone based communications in the last
disaster in Japan and extract meaningful patterns. Second, such patterns and inputs from the
field were used to further develop and better tune a broadcast protocol that self-adapts to satisfy
the communication needs in highly dynamic and unpredictable disaster situations. When
disasters occur, the telecommunication infrastructures are usually disrupted. Consequently, the
lack of communications among authorities, first responders and population causes tragic
results, including massive loss of human lives in US, in Japan and all over the world. The key
characteristic of disaster situations is their unpredictability. Therefore, the premise of this work is
that the technologies for communications should always be ready. Consequently, following
another recommendation to “take advantage of opportunities for adoption of existing
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technology,” the project studied the use of ad hoc wireless communications among cell phones
in disaster situations.
Data Compression in Wireless Sensor Networks (Yao Liang)
Wireless sensor networks (WSNs) are being increasingly deployed for enabling continuous
monitoring and sensing of physical variables of the world. Energy efficiency is of paramount
importance in the design and deployment of wireless sensor networks, as WSN nodes are
typically battery-powered, and in many real physical environments the replacement of batteries
for nodes is either difficult or virtually impossible. In general, radio transmissions and receptions
are most power consuming compared to the energy consumption of node microcontroller and
memory in WSNs. Data compression is a useful technique in the deployments of resource-
constrained WSNs for energy conservation. This project has worked on developing both
compression algorithm and compression framework for WSNs and has resulted in the
development of a new lossless data compression algorithm in WSNs, called Sequential
Lossless Entropy Compression (S-LEC). Compared to existing WSN data compression
algorithms, the proposed algorithm is not only efficient but also highly robust for diverse WSN
data sets with very different characteristics. This project has developed a sophisticated
framework of temporal compression, called Two-Modal Transmission (TMT), and its extension
as a unified algorithmic framework for both lossless and lossy data compression. This research
has also resulted in the development of a novel approach for exploiting spatial correlation in
WSNs based Markov Random Field (MRF) to infer missing observations, which simultaneously
facilitates energy-efficient and robust data collections.
Topology Tomography in Sensor Networks (Yao Liang)
Sensor network topology tomography is essential for routing improvement, topology control,
anomaly detection and load balance. Previous studies on WSN topology tomography are
restricted to either static routing tree estimation or heuristic approaches, which is inadequate in
real-world WSNs due to dramatic wireless channel dynamics. This project studied general WSN
routing topology tomography from indirect measurements observed at the sink, where routing
structure is highly dynamic. The problem was formulated as a novel compressed sensing (CS)
problem, and then a suite of efficient decoding algorithms to effectively recover WSN routing
topology was devised using the indirect measurement at the sink for both reliable and lossy
WSNs. The project analyzed the complexity of the devised algorithms and validated the
approach and algorithms with a real-world outdoor WSN system using CTP for environmental
data collection as well as extensive simulations. One of unique strengths of this work is that the
approach and algorithms are able to reconstruct loops in per-packet paths, which would be very
helpful for WSN diagnosis and performance analysis of routing protocols.
Studies on WSN Deployment, Operations, and Network/Data Management (Yao Liang)
In collaboration with the University of Pittsburgh, this project deployed and experimented on a
real-world environmental WSN testbed for years to thoroughly study important practical issues
in WSN operations, including sensors’ energy characteristics in depth, and WSN network data
and management. Through this research, a novel integrated network and data management
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system for heterogeneous WSNs was developed. Network management of WSNs is one of the
key practical challenges that arise from the increasing number of applications and technologies
deployed. WSN management becomes increasingly important to monitor and ensure that
deployed motes operate correctly and healthily along time. The severe resource constraints of
WSNs have introduced and involved different hardware and software technologies of sensor
networking, being designed for very specific purposes. As a result, users with complex
applications are directly facing the complexity of interacting with diverse technologies from
different manufacturers and specific requirements. This work presents a web-based integrated
network and data management system that is aimed at: (1) systematically supporting
heterogeneous WSNs with a unified management system; (2) presenting a clear separation
between WSN management and application functions; and (3) offering management
functionalities with a clear user interface. This work has developed and deployed a
heterogeneous WSN management system in a real-world WSN testbed for environmental
monitoring.
Open Data Open Modeling Cyberinfrastructure for Geosciences Communities (Yao Liang)
To improve understanding of the complex behaviors of the various processes (e.g., physical,
hydrological) and their interactions involved in the Earth System, as well as the accuracy and
reliability of model predictions of weather, floods, droughts, and climate variability, researchers
need to be able to make good use of the available data across disciplines to improve their
theories, algorithms, models, and validations. However, a large amount of such valuable data
often goes unused, due to the significant overhead of time and effort needed to discover,
access, understand, and prepare the data. Similarly, there are many models available (e.g.,
hydrological/land surface models, routing models), but the complexity of these models
necessitates a long lead time, even for a domain scientist, to learn how to use the models.
Complexities related to individual models, different data requirements of models, and the myriad
data formats, coordinate systems, and resolutions cause huge difficulties for both research and
user communities. This combination of the variety and complexity of models and the usability of
existing diverse data presents one of the most critical challenges in Earth Science. Hence, the
development of an open data and open modeling framework, which can integrate data and
models easily and incrementally for knowledge discovery and management, is fundamentally
important and urgent, not only to the research community and operational professionals, but
also to policy makers and other users. The ultimate goal of this collaborated project is to build
an open data open modeling cyberinfrastructure, which should significantly reduce the time and
effort on the part of users in the preparatory work for data and model comparisons, model
testing and validations, and fundamental knowledge discoveries. In such a framework,
components/modules interact via user-configured open interfaces, so that various scientific
models and data sources can be easily added and composed to interoperate on an open
architecture of cyberinfrastructure, through scientific workflows. The developed prototype, called
as OHMF (Open Hydrospheric Modeling Framework), has now integrated four data agents into
the framework prototype, which already covers a broad range of data sources and data access
protocols: NASA (OPeNDAP-GDS), USGS (REST Web services), CUAHSI HIS (SOAP Web
services), and CUAHSI WaterOneFlow (SOAP Web services). Data are brought in via both
gridded (netCDF) and point time series (WaterML) services, using two major cataloging services
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(NASA GES DISC and CUAHSI). With the help of this OHMF framework, new data agents can
be efficiently developed. This project also developed two model agents for VIC model and a
simple routing model. Any scientific models integrated into the framework can then directly
access those data sources online in an automatic manner, and easily couple with each other in
the OHMF via workflow. The crux of this framework is that it allows scientists to write their own
model agents. Benefits of getting their models into the framework significantly outweigh the
costs of writing their own model agents.
Improving Hydrologic Disaster Forecasting and Response for Transportation by
Assimilating and Fusing NASA and Other Data Sets (Yao Liang)
In this project, the work involves collaboration with researchers in environmental engineering at
the University of Pittsburgh and NASA Data Center on improving PennDOT’s hydrologic
disaster forecasting and response, by using an innovative spatial data fusion and assimilation
framework, and NASA’s satellite data on soil moisture and snow, as well as other data from
WSNs. The main project goal is to develop a hydrologic disaster forecast and response (HDFR)
system, which will forecast severe weather conditions at the road level and thus improve
PennDOT’s decision making capability from its current reactive nature to a more proactive
nature. The approach is through innovative assimilation and fusing of NASA data, radar data,
RWIS road weather data, and other available data to form adequate and coherent multiscale
information for having useful and reliable hydrologic forecasts at the multiple spatial scales. The
development of HDFR system is on-going.
Enhancing NOAA Advanced Weather Interactive Processing System (AWIPS) DSS by
Infusing NASA Research Results for Drought and Other Disaster Management (Yao
Liang)
In this project, the IUPUI PI collaborated with hydrologists at the University of Pittsburgh and
NOAA National Weather Service, as well as researchers at NASA Goddard Earth Sciences
Data Center, to innovatively infuses NASA’s newly available remote sensing data and models
into National Weather Service’s core operation to enhance its decision making and weather
forecasting performance for flooding and drought disaster management. The developed system
has been successfully deployed and tested offline at the Ohio River Forecast Center (ORFC). In
this project, a labeled-tree data integration mode, referred to as DataNode tree, was developed
and based on which a data integration framework HIDE was developed. This systematic
investigation of the statistical behaviors of the spatial similarities and dissimilarities between
NEXRAD (Next Generation Radar) and NLDAS (North American Land Data Assimilation
Systems) precipitation data is the first of its kind, which provides significant insights into these
two widely used data products in various hydrological and climatic studies.
The Internet based Electronic Voting enabling open and fair election (Xukai Zou)
Voting is the pillar of modern democracies. However, examination of current voting systems
(including E-voting techniques) shows a gap between casting secret ballots and tallying and
verifying individual votes. This gap is caused by either disconnection between the vote-casting
process and the vote-tallying process, or opaque transition (e.g. due to encryption) from vote-
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casting to vote-tallying and thus, damages voter assurance, i.e. failing to answer the question:
“Will your vote count?”. This work proposed a groundbreaking E-voting protocol that fills this gap
and provides a fully transparent election. In this new voting system, this transition is seamless,
viewable, and verifiable. As a result, the above question can be answered assuredly: “Yes, my
vote counts!” The new technique is the first fully transparent E-voting protocol which fills the
aforementioned gap. The trust is split equally among all tallying authorities who are of conflict-
of-interest and will technologically restrain from each other. As a result, the new technique
enables open and fair elections, even for minor or weak political parties. It is able to mitigate
errors and risk and detect fraud and attacks including collusion, with convincingly high
probability 1 − 2−(m−log(m))n (n: voters and m ≥ 2: candidates). It removes many existing
requirements such as trusted central tallying authorities, tailored hardware or software, and
complex cryptographic primitives. In summary, the new voting technique delivers voter
assurance and can transform the present voting booth based voting and election practice.
Besides voting and elections, the new technique can also be adapted to other applications such
as student class evaluation, rating and reputation systems. The project will continue to
investigate related issues such as vote-selling and voter-coercion and develop a practical online
e-voting system and also adapt it to various university applications such as tenure and
promotion voting and class evaluation.
Revocable, Interoperable and User-Centric (Active) Authentication Across Cyberspace
(Xukai Zou)
User authentication is the first guard of any trustworthy computing system. This work addresses
funda- mental and challenging user authentication and universal identity issues and solves the
problems of system usability, authentication data security, user privacy, irrevocability,
interoperability, cross-matching attacks, and post-login authentication breaches associated with
existing authentication systems. It developed a solid user-centric biometrics-based
authentication model, called Bio-Capsule (BC), and implemented an (active) authentication
system. BC is the template derived from the (secure) fusion of a user’s biometrics and that of a
Reference Subject (RS). RS is simply a physical object such as a doll or an artificial one, such
as an image. It is users’ BCs, rather than original biometric templates, that are utilized for user
authentication and identification. The implemented (active) authentication system will facilitate
and safely protect individuals’ diffused cyber activities, which is particularly important
nowadays, when people are immersed in cyberspace. Biometrics is becoming a promising
authentication/identification method because it binds an individual with his identity, is resistant to
losses, and does not need to memorize/carry. However, biometrics introduces its own
challenges. One serious problem with biometrics is that biometric templates are hard to be
replaced once compromised. In addition, biometrics may disclose user’s sensitive information
(such as race, gender, even health condition), thus creating user privacy concerns. The
proposed approach is the first elegant solution to effectively address irreplaceability, privacy-
preserving, and interoperability of both login and after-login authentication. The proposed
methodology preserves biometrics’ robustness and accuracy, without sacrificing system
acceptability for the same user, and distinguishability between different users. Biometric
features cannot be recovered from the user’s Biometric Capsule or Reference Subject, even
when both are stolen. The proposed model can be applied at the signal, feature, or template
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levels, and facilitates integration with new biometric identification methods to further enhance
authentication performance. The project will continue to develop BC based active authentication
and privacy-preserving BC computation using secure two party computation.
NSF REU Site: Enhancing Undergraduate Experience in Mobile Computing Security
(Shiaofen Fang, Mohammad Al Hasan (Hasan), and Xukai Zou)
This REU Site project enrolls 10 undergraduate students each year in an intensive 10-week
summer research program, to be hosted at Indiana University-Purdue University Indianapolis
(IUPUI), on mobile computing security. The students will work closely with faculty mentors from
the Mobile Computing Security Research laboratory (MCSR) and the Trusted Electronics and
Cloud Obfuscation research and education center (TECO) at IUPUI. The goal of this program is
to inspire underrepresented minorities and students from institutes with limited research
opportunities to pursue advanced education and professional careers in Computer and
Information Science and Engineering (CISE). To reach that goal, the program is designed to: (1)
Familiarize the students with the latest developments in mobile and cloud computing
technologies through hands-on projects; (2) Increase the students awareness of recent
advancements in academic research in these areas by extensive literature review, presentation,
and discussion; (3) Help the students make informed decisions on, and prepare for, advanced
graduate studies and professional careers in CISE by working on research projects under one-
on-one mentoring.
MovingCloud: Create Moving-target Defense in Cloud by Learning from Botnets (Xukai
Zou)
The research objective of this project is to investigate recent botnets and design a moving-target
defense framework to improve resiliency and harden existing static infrastructure in CLOUD
systems. Two facts set the stage for the proposed research: 1) making the physically static
cloud a Moving Target, by designing secure logic operation schemes, to increase the
robustness and survivability of the networks; 2) Botnets, which are networks of computers that
are compromised and controlled by an attacker, show some sophisticated developments in the
recent years, greatly increased the agility and polymorphism of the botnets, and reduced the
defenders ability to disrupt the operations in botnets. The project plans to learn from the
attackers schemes, and create diverse, continually shifting, and changing over time CLOUD
environment, to reduce the attackers understanding of the systems and their ability to launch
attacks, while maintaining satisfactory CLOUD service performance. The resulting
MovingCLOUD scheme will increase the complexity and costs for the attackers, limit the
exposure of vulnerabilities and opportunities for attack, and increase CLOUD systems
survivability.
Building A Secure Video Streaming Framework for Dynamic and Anonymous Subscriber
Groups (Xukai Zou)
Secure video content distribution is a key aspect in the deployment of Telepresence Services
and Video on Demand, two critical applications for the ecosystem targeted by Cisco products.
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Efficient mechanisms and systems need to be developed to guarantee confidentiality and
controlled access to a broad range of broadcast video streams. At the same time, an effective
framework for secure video content distribution should also guarantee subscribers privileges to
access video streams matching their respective subscription and on-demand requirements. In
this project a Secure Video Stream Framework for dynamic and anonymous subscriber groups
will be built, by employing an innovative approach called Access Control Polynomial (ACP). The
framework will effectively address the underlying challenges of secure video stream
broadcasting and guaranteed access, anonymity, dynamicity, granularity, and scalability.
Evaluation of Clinical and Genomic Information Privacy Risks from Inference Attacks
(Xukai Zou)
This project examines the quantitative relationships between clinical and genomic information
disclosure and associated privacy risks due to inference attacks. For inference attacks, the
inference of private personal identity and other personal information is referred to without the
information owners’ explicit consent or knowledge. In translational medical studies, identifiable
personal information is usually anonymized and protected using a set of high-level guidelines.
However, there is no explicit guarantee that such anonymization is performed to the best
interests of research participants, especially with the increasing demand for open access of
biobanks by researchers worldwide and, in some cases, patients themselves who are allowed to
gain access to their own research results. Nor does there exist a method that can help
researchers and biobank stakeholders gauge the risks for inference attacks, if the anonymized
clinical database is compromised due to security leaks.
Education Research
Members: Andy Harris, Michele Roberts, Lingma Acheson, Snehasis Mukhopadhyay, Judy
Gersting, Xukai Zou, Shiaofen Fang, Mohammad Hasan
Collaborative Units: Faculty members of the CS department and Kathy Marrs from the School
of Science Dean’s office, Feng Li (CIT), Chris Lapish (Psychology), Giovanna Guidoboni (Math)
Along with full teaching loads, the service committee has supported the department mission with
research pursuits in two main categories: Computer Science education and Serious Gaming. In
CS Education, grants have allowed the department to significantly lower DWF rates in gateway
courses, integrate new IEEE guidelines into existing curriculum, seed applied coursework with
project-based learning, and utilize peer-based, team learning to bolster knowledge gains for
undergraduates in live and online course sections. An ongoing partnership with Riley Hospital
explores the impact of serious gaming to teach key skills for better managing childhood
diabetes. These grants have both improved the quality of pedagogy the department offers it
students, as well as extended the range of available research opportunities for undergraduate
talent.
AP CS Principles course (Michele Roberts)
The AP Pilot grant was awarded based on a syllabus submission for the AP CS Principles
course pilot. The submitted syllabus proposed balanced treatment of the so-called “Big Ideas” in
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computer science and included innovative project work to support and document student
inquiry. In addition, the described course infrastructure outlined the use of peer led team
learning, included Honors credit opportunities and developed service learning opportunities
through community outreach programs.
Curriculum Development for CS Principles course (Kathy Marrs, Snehasis Mukhopadhyay,
Michele Roberts)
This internal grant was a Curriculum Grant awarded by the Center of Teaching and Learning.
The grant provides support for detailed curriculum development to support the CS Principles
course, including the assembly of resource materials, exploration of robots for use in the
programming unit, and descriptions and assessment materials for course project work.
Redesign and Curriculum Enhancement of CSCI N341 (Lingma Acheson)
The grant was awarded by the IUPUI Center of Teaching and Learning for the redesign of the
course CSCI N431 “E-commerce with ASP.NET” to incorporate real-world, client-specified
projects originating from corporate, not-for-profit, educational and service-oriented entities. The
project involved a design stage from August 2013 till December 2013, and an implementation
stage from January 2014 till May 2014. During the design stage, new course materials,
documentations were developed; candidate projects were identified, and the teaching assistant
was selected. At the implementation stage, projects were carried out smoothly and feedback
was collected timely. At the end of the semester, a total of 6 real-world projects were completed,
some of which will have significant positive impact to the community. This new approach also
significant improved students business communication skills, team working ability, time
management skills and presentation skills, which are vital in a working environment yet were
missing in the old lecture-based learning style.
REU Site: Enhancing Undergraduate Experience in Mobile Computing Security (Xukai
Zou, Shiaofen Fang, Mohammad Hasan, Feng Li—CIT)
This REU site project enrolls 10 undergraduate students each year in an intensive 10-week
summer research program, hosted on the IUPUI campus, on mobile computing security. The
students work closely with faculty mentors from the Mobile Computing Security Research
laboratory (MCSR), the Trusted Electronics and Cloud Obfuscation research and education
center (TECO), and Center for Visual Information Sensing and Computing (VISC) at IUPUI.
The goal of this program is to inspire underrepresented minorities and students from institutes
with limited research opportunities to pursue advanced education and professional careers in
Computer and Information Science and Engineering. Up until now, the Department has hosted
this intensive summer camp for two years and the participating students have shown a very
positive and encouraging response after attendance. Particularly, three papers from these
students have been accepted as conference papers to be presented and published.
Undergraduate Curriculum in Interdisciplinary Computational Science (Snehasis Mukhopadhyay, /Chris Lapish, Giovanna Guidoboni)
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Computers constitute one of the technological innovations that have had the most impact in all
spheres of human activity and endeavor in the entire history. It is a common knowledge now,
both in academia and popular media, that computation forms an integral part of all scientific
disciplines (http://www.nytimes.com/2001/03/25/weekinreview/25JOHN.html). In this spirit, this
project proposes to create a set of Interdisciplinary Computational Tracks for the B.S. degree.
Among other disciplines, science and engineering are probably the ones that went through the
most major transformations with the advent of computers. The President’s Information
Technology Advisory Committee (PITAC) in a 2005 report to the President of the United States,
noted that “Together with theory and experimentation, computational science now constitutes
the ‘third pillar’ of scientific inquiry” and goes on to say, “In industry, computational science
provides a competitive edge by transforming business and engineering practices”.
Computational biology, computational chemistry, computational physics, computational
environmental science, computational neuroscience, etc., are being recognized as very
promising inter-disciplinary research areas capable of leading to fundamental discoveries in
science, medicine, and technology. Yet, while recognizing that computational science will be
one of the major drivers of the global economy in the twenty first century, the President’s
Information Technology Advisory Committee (PITAC) also noted in 2005 the need for new
academic programs and institutional support to train the next generation of computational
scientists (http://www.nitrd.gov/pitac). These scientists will not be mere users of computational
tools, but will be leaders in employing computational and mathematical thinking to solve
complex scientific and engineering problems that defy traditional non-computational solutions.
On behalf of the iMMCS (Institute for Mathematical Modeling and Computational Science)
institute, Dr. Mukhopadhyay made a formal proposal to the SOS Undergraduate Education
Committee (UEC) to create a set of six so-called “Computational” tracks (Computational
Biology, Computational Chemistry, Computational Earth Sciences, Computational Forensic
Sciences, Computational Neuroscience, and Computational Physics) in the B.S. program.
These tracks will have standardized course plans, while still allowing for some individualized
customization. During the Fall 2013 semester, the School of Science Undergraduate Education
Committee approved the creation of 6 such computational tracks in concept. The Computational
Biology track has been approved by the Biology Department's undergraduate committee. The
Physics Department is also supportive of a Computational Physics option.
Three salient features of these course plans are:
(i) No new courses are included or proposed (except for 2-3 in the Computational
Neuroscience track); these are almost all existing courses.
(ii) All plans have a substantial number of credit hours for free/advanced electives. By
utilizing 9 of them for graduate/undergraduate dual-level coursework, it is possible
for the students to complete an integrated B.S. and M.S. degree in Computer
Science, in some cases Mathematics, or the other relevant Science
Department(s) roughly in 5 years, as is currently available to Computer Science
students in the school.
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(iii) It is believed that these programs will provide a quality education to students in
modern sciences, which will make them attractive to potential employers and
prepare them well for advanced research careers in science.
A presentation was made by Profs. Snehasis Mukhopadhyay (Computer Science), Chris Lapish
(Psychology), and Giovanna Guidoboni (Mathematics) to the School of Science Chairs’ Council
in Summer 2014, providing the rationale as well as a tentative structure and outline for 4 such
tracks (Computational Biology, Computational Physics, Computational Chemistry, and
Computational Neuroscience). The goal is to make interdisciplinary computational sciences a
signature of the School of Science.
Student Learning Objectives Across Applied CS Curriculum (Michele Roberts)
The purpose of this internal grant was to update student learning objectives across the applied
curriculum. As a result, twelve courses were updated to explicitly link to the university Principles
of Undergraduate Learning and the 2008 release of the joint ACM/IEEE curricula committee
standards.
Improvement of DWF rates for CSCI N241 (Michele Roberts)
The purpose of this internal grant was to analyze and improve DWF and progression rates for
N241: Fundamentals of Web Development, which functions as the gateway course for the
applied computing curriculum. As a result of this effort, N241 DWF rates dropped by eight
percent and retention (as measured by student movement to the next course in the series,
N341: Client Side Programming), improved by six percent.
Development and Implementation of Web-based Modules for a Diabetes education
Program in the Pediatric Outpatient Setting (Stancombe, K, Andrew Harris)
This grant investigates use of web and mobile games to motivate patient education among
young people with type I Diabetes. This project is a joint effort with the Diabetes team at Riley
Hospital for Children. The team created a number of iPad games designed to teach specific
patient education skills including understanding glucose monitor readings (with a game similar
to Angry Birds) to nutrition and ketone management, and an adventure game which explores
difficult areas including depression, eating disorders, and bullying. The project has also created
a tracking program to manage patient knowledge and to determine the efficacy of the project.
Currently the project is halfway through completion, and the first three games are currently
undergoing beta testing in the clinical setting.
In summary, the faculty members in the Department are actively pursuing various research
directions and many of these are funded via external and competitive sources. This positive
trend is expected to continue with the hiring of new and high-caliber faculty members.
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Chapter 9 Service
The Department’s faculty members have continued to maintain a high level of service—at the
School, University, professional and community levels. Some highlights of each faculty
member’s service record are listed below; more details may be found in the faculty CVs in the
Appendix.
Ms. Acheson's service to IUPUI is focused on international collaboration, recruitment and
advising, and support to various campus events. Upon joining the Department in 2007, Ms.
Acheson immediately started her exploration and development in international collaborative
programs with universities and high schools in China for the purpose of international student
recruitment and joint research. Working with the Department, the School of Science Dean's
Office and the IUPUI Office of International Affairs, she visited many universities and high
schools in China and established a 2+2 joint degree program with the Sun Yat-sen University, a
3+2 student transfer program with the Changzhou Institute of Technology, and a joint research
program between the Department, the IUPUI Department of Tourism and Convention
Management and Ball State University. She also coordinated multiple delegation visits from
China and recruited many students to IUPUI. Ms. Acheson serves as the International Student
Advisor in the Department and is also involved in domestic student recruitment and advising.
She has been active in department, school and university events, such as CS Day, the Science
Day, the Campus Day, and the International Festival. Ms. Acheson was the recipient of the
2009 IUPUI Irwin Experience Excellence Award.
Dr. Dundar has been privileged to serve on the Graduate Committee of the Department (2008-
2012), the Technology (2008-2009), Academic Affairs (2009-2012), and Appeals Committees
(2012-2013) of the School of Science. He served as a member of the Organization Committee
for the ACM SIGKDD conference in 2009. He also served as a Program Committee member for
the ACM SIGKDD in 2012, IEEE ICDM in 2012 and 2014, SIAM SDM in 2013 and 2014, and
SPIE Medical Imaging/Digital Pathology in 2013. He served as a mail reviewer in 2009 and as a
panelist in 2012 for the NIH. He also served as a panelist for the NSF IIS division in 2012 and
2013. As a passionate promoter of math and science education at the K-12 level, he has served
since 2008 as a board member of the Indiana Math and Science Academy (IMSA) charter
school, which has two locations in the Indianapolis area.
Dr. Durresi has been very active in organizing international conferences and workshops. He
was the Chair of the 13th International Conference on Network Based Information Systems
(NBiS 2010), 23rd IEEE Advanced Information and Networking Applications Conference (AINA
2009), and the International Conference on Availability, Reliability and Security (ARES 2009).
He was the Program Chair of the 12th International Conference on Network Based Information
Systems (NBiS 2009), held at IUPUI campus in Indianapolis and the program vice chair for
Security and Trustworthy Computing of the 17th IEEE International Conference on Parallel and
Distributed Systems (ICPADS 2011). He has founded and co-chaired in continuation the
following workshops: International Workshop on Trustworthy Computing (TwC 2012-14),
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International Workshop on Bio Computing (BioCom) 2008-12, International Workshop on
Advances in Information Security (WAIS) 2007-12, International Workshop on Heterogeneous
Wireless Sensor Networks (HWISE) 2005-12. Dr. Durresi has been on the editorial boards of
the following journals: Ad Hoc Networks Journal since 2004, Journal of Ubiquitous Computing
and Intelligence since 2005, Journal of Network and Computer Application since 2008,
Transactions on Networks and Communications since 2009. Dr. Durresi has served as a
member of the University Sabbatical Committee 2013-14; School of Science Research
Committee 2007-10, 2012-13. Committee service in the Department includes: Advisory
Committee (elected position) 2011-present, Primary Committee (Promotions and Tenure
Committee) 2011-12, Graduate Committee 2007-08, and Undergraduate Committee 2008-12.
Dr. Fang has served as a panelist for the National Science Foundation (NSF) many times, and
has been a regular proposal reviewer for the National Institutes of Health (NIH) and other
federal and foreign funding agencies. He regularly serves as a program committee member for
international conferences such as VRST, CGI, and VRCAI. He was a regular co-chair of the
Workshop on Bio-Computing, and a Keynote Speaker for the 2011 International Conference on
Remote Sensing, Environment and Transportation Engineering. Prof. Fang has also served on
numerous campus, university and school level committees such as the IU Strategic Initiatives
(informatics Working Group), Department of Physics Review Team, Purdue System Distance
Education Working Group, Search Committee for School of Science Associate Dean (Chair),
and Search Committee for the School of Informatics Chair.
Dr. Hasan’s services include program committee member of multiple tier-one data mining and
information systems conferences, such as the ACM SIGKDD, IEEE ICDM, ACM CIKM, SIAM
SDM, and IEEE BigData, and reviewer of various data mining and knowledge discovery journals
including the IEEE Trans TKDE, Springer DMKD, ACM Trans KDD, VLDB journal, and IEEE
Trans TNNLS. He also frequently serves on the NSF grant review panel in divisions of the IIS,
BIgData, and SBIR/STTR programs. He is currently serving as the Publicity Chair of SIAM SDM
2015 Conference, the program chair of BigGraph Data 2015 Workshop, and an Award
Committee member for the ACM SIGKDD dissertation award. He also serves as a member of
the Graduate Committee of the Department, and the Library Committee of the School of
Science. He is a member of IEEE, ACM, and ACM SIGKDD.
Dr. Hill has served on the department’s Technology Committee (2009~2011) and
Undergraduate Committee (2013~present), as seminar co-coordinator (2009~2010), co-planner
for CS Day @ IUPUI (2011~2013), and faculty advisor for Computer Science Club (2012~2014).
Dr. Hill was appointed to the 2013~2014 Advisory Board for IUPUI’s Department of Continuing
Education. Dr. Hill was invited to give keynote talks at CS Day @ IUPUI in 2011; Softec 2011 in
Kuala Lumpur, Malaysia; and IU’s HBCU STEM Summer Scholars Institute Closing Luncheon.
He has been invited to speak about his research with trustees, graduate students,
undergraduate students, and the general public at Science on Tap. Dr. Hill has been very
involved in professional organizations and related groups. He has co-organized workshops and
student research competitions, been invited to be committee member and session chair for
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conferences and workshops, chaired panel discussions, served on panel discussions,
and reviewed for panel, journals, conferences, book chapters, and books.
Dr. Liang has been on the Editorial Board for International Journal of Distributed Sensor
Networks, and The Open Cybernetics and Systems Journal. He has served on the program
committees for major international conferences in networking and communications, including
ICC, WCNC, LCN, HPCC, and PIMRC. He has served as a reviewer for many primary
international journals (e.g., IEEE Journal on Selected Areas in Communications, IEEE
Transactions on Vehicular Technology, IEEE Communications Letters, Ad Hoc & Sensor
Wireless Networks, IEEE Transactions on Systems, Man, and Cybernetics (Part B), IET
Communications, IET Signal Processing). He served as an NSF review panelist in 2010. At
IUPUI, he has served on the Steering Committee (2010-2014), Appeals Committee (2007-
2010), and Research Committee (2010-2011) of the School of Science; he has served on the
Department’s Graduate Committee (2008-2014), Undergraduate Committee (2007-2008),
Faculty Search Committee, Primary Committee, and P&T Committee.
Dr. Mukhopadhyay served as the School of Science Faculty President-Elect, President, and
then Past President for the academic years 2011-2012, 2012-2013, and 2013-2014,
respectively. From 2011 to 2014, he was an appointed member of the IUPUI Undergraduate
Curriculum Advisory Committee (UCAC). He was a member of the School of Science Promotion
and Tenure (Unit) Committee in 2011-2012. He has been the Chair of the Computer and
Information Science Department Promotion and Tenure (Primary Committee) since 2011. He
has served on the Department's faculty recruitment committee in 2014. He has been a member
of the School of Science Nominations and Awards Committee since 2011. He is a member of
and was a past chair of the Computer and Information Science Department's undergraduate
committee. He has been serving on the IUPUI Computing Curriculum Coordination Council (C4)
since its inception in 2011. He has also been serving as an IUPUI Honors College Scholarship
Interviewer since 2010. Dr. Mukhopadhyay has served as the Director of the IUPUI Signature
Center on Biocomputing and is currently serving as a Co-Director of the School of Science
Institute for Mathematical Modeling and Computational Science (iMMCS). Additionally, Dr.
Mukhopadhyay served as an NSF panel member during the years 2009 and 2011. He has been
a reviewer for NIH. He is an editorial board member of the ISSN Artificial Intelligence Journal
and the Journal of Biomedical Science. He has reviewed papers for numerous journals and
conferences including IEEE Transactions on Neural Networks, IEEE Transactions on Systems,
Man, and Cybernetics, Artificial Intelligence in Medicine Journal, etc., and served on the
program committees of several international conferences including IEEE Systems, Man, and
Cybernetics Conference. He has been recruited to serve as a General Chair of the ACM
International Conference on Information and Knowledge Management (ACM CIKM) in 2016. He
has been a member of the IEEE Technical Committee on Intelligent Control (TCIC) since 1998,
and has been recently recruited to serve in the IEEE Technical Committee on Soft Computing
(TCSC).
Dr. Raje, in addition to serving as the Associate Chair of the Department, has been serving as
the Graduate Program Director (and hence, the chair of the Graduate Committee of the
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Department) since 2006. He has also served as a member and the chair of the Faculty Search
Committee on multiple occasions since 2006. He was a member (2006-08 and 2010-2014) and
the chair of the Primary Committee (2010-11) of the Department. Dr. Raje has also been a
member of multiple search committees of the Department for different staff positions. Dr. Raje
has been also a member (2010-14) and the Chair (2012-14) of the Unit Committee of the
School of Science. The other School of Science Committees oh which he has served are:
Graduate Education Committee (2013-14), Ad-Hoc Committee for P & T Document (2012-13),
Dean Search Committee (2007-08, 2010-11), Graduate Affairs Committee (2003-08), Associate
Dean Search Committee (2008), Graduate Training Strategic Working Group (2007) and
Faculty Grant Workshop (2006). Dr. Raje has also rendered his services at the campus (IUPUI)
level by acting as a Top 100 Judge (2012-14) and a member of the campus Tenure and
Promotion Committee (2010-11). Dr. Raje has been a regular panelist at the NSF and has acted
as an external reviewer (in some cases as a Tenure and Promotions Reviewer) for many
universities such as the University of Colorado at Denver, the University of Washington at
Tacoma, the Cleveland State University and the University of UAE. In addition, he has served
as an external PhD examiner for the following universities: the Deakin University, Australia
(multiple times) and the Tezapur University, India. Dr. Raje also acts a reviewer for many
esteemed journals (e.g., Journal of Parallel and Distributed Computing, Concurrency and
Computation, and the ETRI journal) and conferences (e.g., IEEE EDOC, SEKE, and IEEE
HPCC) on a routine basis. He has also served on numerous conference program committees
since 2006 and acted as the Local Arrangements Chair (ACM SPLASH Conference, 2014), the
Program Vice-Chair (IEEE HPCC 2008 and 2010), the Program Vice-Chair (IEEE ICPADS
2008) and a Track Chair (NBiS, 2009). He is currently a member of the Editorial Board for the
following journals: CSI Transactions on ICT, Software Engineering: An International Journal,
International Journal of Information Technology, Communications and Convergence and the
International Journal of E-adoption.
Dr. Song has served on technical program committees for numerous premier international
parallel computing conferences such as SC, IPDPS, CCGrid, and Euromicro conferences. He
has also been a regular reviewer for top journals of JPDC, TPDS, ParCO, and Journal of
Supercomputing. He organized the first International BigGraph workshop in conjunction with
the IEEE Big Data 2014 conference. At IUPUI, Dr. Song has been a member of the School of
Science Technology Committee since 2013.
Dr. Tuceryan has served on various Departmental committees (Primary Committee member
and chair, faculty search committee chair, Undergraduate Committee chair, Advisory
Council member), School of Science committees (Steering Committee member, secretary,
president, and past president of the school’s faculty, unit committee member, and school’s
Undergraduate Educational Policies committee member), and university level committee
(Promotion and Tenure committee). He has also served on various professional and
community service panels and committees (associate member of the Scientific Working
Group on Imaging Technology, member of program committees as well as reviewer on a
number of professional conferences of the ACM and IEEE, reviewer of grant proposals to
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National Institute of Justice, the Research Grants Council of Hong Kong, and the Innovation
and Technology Support Programme of Hong Kong).
Dr. Tsechpenakis has frequently served as a reviewer for the primary international journals and
conferences of the Computer Vision, Machine Learning, and Biomedical Imaging/Engineering
societies (e.g., IEEE Trans PAMI, IEEE TMI., IEEE TBME., Elsevier CVIU, IJCV, ICCV, CVPR,
ECCV, MICCAI, ISBI). In 2010, he served as a reviewer on an NSF panel. Since 2011, his lab
has been hosting internships for K-12 students (2-4 students every summer). He has served on
the Undergraduate (2010--2011) and Graduate (2013-2014) committees of the Department, and
on the Research (2011--2012), Diversity (2011--2012), and Library (2012--2013) Committees of
the School of Science.
Dr. Xia has served on the program committee on many international conferences such as the
International Conference on Collaborative Computing (CollaborateCom), the IEEE International
Conference on Computer and Information Technology (CIT) and the IEEE International
Conference on Computational Science and Engineering (CSE). She has also served as a
reviewer for high impact journals in databases and data mining such as IEEE Transaction on
Knowledge and Data Engineering (TKDE), IEEE Transactions on Parallel and Distributed
Systems, ACM Transactions on Database System (TODS), ACM Transaction on Knowledge
Discovery from Data, Journal of Knowledge and Information Systems, Journal of Data and
Knowledge Engineering, etc. She served on NSF panels in 2007, 2009 and 2011. She has also
served on the Graduate Committees of the Computer and Information Science Department, and
on the Research Committee, Award Committee and Library Committee of the IUPUI School of
Science.
Dr. Zheng has served as a reviewer for ACM TOMCCAP, IEEE MM, IEEE VCG, IJCV, VCIP,
CVIU, IPSJ Trans. PVA, MVA, IEEE Trans. ITS, Sensor, IEICE, JMPE, IJHC, CAVW, Digital
Content Technology and Application, and the International Journal on Wireless and Mobile
Computing. He has served on the program committee for ICPR, ICME, ACM MM,
CYBERWORLD, IROS, IEEE ICRA, ACCV, ACPR, VSMM, OMNIVISION and Digital Heritage.
In addition, he has served on grant review panels for NSF, NPRP and the US-Israel Binational
Science Foundation, and he has hosted several international researchers. He has served on
the Department’s Undergraduate and Advisory committees and the IUPUI School of Science
Library and Undergraduate Education Committees.
Dr. Zou has frequently served as a reviewer for the primary international journals and
conferences related to cryptography and information and network security (e.g., IEEE Trans.
TDSC, IEEE Trans. TIFS, IEEE Trans. TPDS, ACM Trans. TISSEC, and INFOCOM) and as an
invited reviewer for Computing Review. He serves as an associate editor for several
international journals (e.g., International Journal of Security and Networks, International Journal
of Computer Applications). He also served as an NSF panelist in 2009 and 2011 and an NIH
grant external reviewer in 2009. He has been in charge of the weekly Department research
seminars (2008-2014), and has also served on the Teaching and Assessment (2007-2010) and
Appeal (2013-2014) committees of the IUPUI School of Science.
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As can be seen from the above descriptions, the Department’s faculty members have been and
continue to be extremely active in all areas of service, both within IUPUI and the broader
academic field, including at the international level. Faculty members have served in various
leadership capacities, including reviewer, panel member, organizer, mentor, conference host
and chair, etc. These efforts will certainly continue and, in fact, strengthen as newer
approaches will be identified to increase faculty service opportunities in the future.
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Chapter 10 Challenges and Future Directions
Although the Department has made tremendous progress in all aspects of teaching, research
and service since the last review in 2007, major challenges are still ahead that require timely
and determined actions.
a) Research Sustainability. The success in recruiting high quality faculty members and the
emphasis on grant proposal activities have led to a remarkable upward trend in faculty
research productivity during the past a few years. Sustaining this level of research output,
however, is a major challenge as grant funding becomes increasingly difficult and the need
for recruiting and supporting more high quality Ph.D. students is growing. New and
innovative research initiatives and greater and more balanced faculty research efforts will be
needed to sustain long term research success and to improve per faculty research output.
b) Teaching Scalability. Increased enrollments in both undergraduate and graduate courses
in recent years have led to a new challenge in the ability of the Department to deliver quality
education to an increasingly larger student population, and to improve retention rates at all
levels. Since Computer Science enrollments often depend on national trends and market
forces, flexibility and scalability in course offering and delivery is a critical factor in
maintaining the long term financial health of the Department. The faculty needs to be
innovative in developing better teaching methods, improving curriculum structures, and
providing a flexible and scalable mechanism in course offering and delivery.
c) Outreach and Recruitment. As a field with close industrial relevance, the Department has
not made and sustained sufficient progress in building industrial connections, both in terms
of research collaborations and student co-op and internship partnerships. There is also a
need to make a greater effort in working with local high schools to create a Computer
Science education community, which can also help in the recruitment of local high-caliber
high school students.
A 5-year strategic planning exercise was conducted in 2007. In 2012, when the Department
evaluated its performance against the goals of the first strategic plan, it was clear that the
Department has exceeded all the goals set in 2007. In 2012, a new 5-year strategic plan with
much higher goals and aspirations was created. While the Department has already made some
progress in many of these goals, there is much work to be done. The following are the priority
future directions that will be of focus in the next few years.
1. Growth of Faculty Size. While the Department has gained strength in data mining and
visualization/imaging, it still lacks the critical mass in other areas, in particular, high
performance computing and network security. The faculty has also expressed a strong need
to recruit an active researcher specializing in the domain of Computer Science educational
research. With these critical research needs and an increasingly larger student population, it
is the asipiration of the Department to reasch a size of about 25 full time faculty members
(including lecturers) in the next few years. The faculty believes that the Department is on its
way to becoming nationally competitive in innovations in both research and education.
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Timely investment is critical in establishing the Department as a flagship unit of the IUPUI
campus.
2. Computer Science Education Research. Computer Science education in U.S. high
schools and at beginning college levels has not kept pace with national needs for computing
professionals. Despite this perceived national urgency, there is very little investment in the
Department on CS education research. As a strategic initiative, the Department would like to
establish a research program in the area of Computer Science Education Research. In
addition to recruiting a tenure-track faculty member in this field, existing education research
activities within the Department as well as varius STEM education initiatives on the campus
will be leveraged. Such an approach will enable the Department to become one of the
national leaders in this field.
3. Improving Retention. In the past few years, the Department has made major revisions and
additions to its academic programs and curricula. The new degree programs and curricula
are more comprehensive and diverse so that they can serve a wider spectrum of students.
In the next few years, the Department would like to focus more on how to better deliver its
programs and courses to various student populations. One major goal is to improve the
retention rates at all levels. This will involve innovations in teaching methods and the
enhancement of student learning assistance programs. The Department would like to
introduce recitations and peer-led team learning to more courses, and experiment with
different learning assistance techniques for different types of courses. The Department
would also consider developing a new quality control and assessment mechanism for online
courses, so that it can make better decisions on whether and how to expand the current
online course and program offerings.
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Appendices
Disciplinary Differences of Undergraduate Computing Programs at IUPUI
PURDUE UNIVERSITY - SCHOOL OF SCIENCE
Computer and Information Science (CSCI)
CSCI at IUPUI teaches the foundations of computing and information processing along with the
necessary scientific and practical skills to prepare students for the demands of the current and
future computing-driven society. Graduates are able to devise, analyze, improve upon, and
experiment with algorithms, system design principles, and software solutions for a wide variety
of problems and to apply these skills to specific real-world application areas such as biology,
medicine, engineering, environmental systems, business and industry, cyber security, and
forensics. Undergraduate research is encouraged so that students may contribute to, as well as
benefit from, the frontiers of computing.
INDIANA UNIVERSITY - SCHOOL OF INFORMATICS AND COMPUTING
Informatics
The undergraduate program in Informatics, combining principles from information systems,
computer science, psychology, and sociology, prepares students to tackle current-day problems
in business, healthcare, science, law, art, and entertainment. In the core set of classes, students
study information management, application development human-computer interaction, and the
legal and social aspects of information and technology. Students also complete a concentration,
which involves the application of informatics to a field of study of their choice. Popular choices
include business, human-computer interaction, media arts, biological and health-related
sciences, and legal informatics.
Media Arts and Science
In the Media Arts and Science program, students study and practice the use of digital media to
communicate, educate, engage, or entertain. The program explores the fundamentals of
communication and digital storytelling. Many courses in the program are project-based, allowing
students to become fluent in the use of contemporary tools for producing Web sites, games, 3D
motion graphics, and videos. Students also learn to develop software applications for the
desktop, the Web, and mobile devices. The program is flexible, allowing to students to choose
the path that best matches their career goals. The program also fosters the skills and qualities
prized by employers in the 21st century workplace – skills for communication, teamwork, and
productivity.
PURDUE UNIVERSITY - SCHOOL OF ENGINEERING & TECHNOLOGY
Computer Engineering
Computer Engineering is the integration of the fields of electrical engineering and computer
science to develop computer-based systems. Students get training in electrical engineering,
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software design, and hardware-software integration. Computer engineering students study
many hardware and software aspects of computing. Computer engineers are usually involved in
writing software and firmware for embedded microcontrollers, designing VLSI chips, designing
analog sensors, and designing mixed signal circuit boards. Computer engineers can work on
computer controlled mechanical devices, such as robots, which involved the control and
communication of motors and sensors. Computer engineering students are allowed to choose
areas of in-depth study in different percentage mixture of hardware and software in their junior
and senior year.
Computer and Information Technology (CIT)
CIT students learn to identify, design, implement, and manage applied software and hardware
solutions to business problems using current and emerging technology. The CIT program
creates IT professionals who can employ and manage technology to best meet the information
management needs of an organization. Students receive instruction in both front-end and back-
end technologies. The CIT program is centered on hands-on experience and real-world
problem-solving with experiential learning incorporated throughout the curriculum. After a
thorough grounding in fundamentals, CIT students select one or more of 4 concentration areas:
networking systems, information security, Web and application development, and data
management. CIT - we make IT work.
Computer Graphics Technology (CGT)
CGT prepares students to become the finest practitioners, managers, and leaders in the field of
applied computer graphics technology and digital communication. Graduates are creative and
technological problem solvers. Graduates gain proficiency in two-dimensional, three-
dimensional, interactive, and time-based principles of computer graphics as they relate to
practical applications demanded by business and industry in Indiana, the nation, and the world.
An innovative leader in its field, CGT provides practical experience through learning, discovery,
and engagement on a domestic and international basis.
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Bylaws (Approved December 19, 1997)
(Reviewed November 21, 2003)
(Reviewed Jan 23, 2009)
THE DEPARTMENT OF COMPUTER & INFORMATION SCIENCE
1 MEMBERSHIP
1.1 ADMINISTRATIVE UNITS
“University” shall mean Indiana University, Purdue University, or IUPUI as appropriate for the
context. “School” shall mean the School of Science at IUPUI and “Department” shall mean the
Department of Computer & Information Science in the School.
1.2 THE FACULTY
1.2.1 Composition of the Faculty of the Department
1.2.1.1 The regular faculty shall consist of all persons who hold appointments in the Department
and are faculty according to the Indiana University Handbooks [1, 2].
1.2.1.2 The graduate faculty consists of the regular faculty who have graduate standing in the
University [5].
1.2.1.3 The honorary faculty shall consist of all other persons who hold academic appointments
in the Department or whom the regular faculty determine according to the Bylaws of the School
[3] that pertain to honorary faculty.
1.2.1.4 The emeritus faculty shall consist of all persons appointed in the Department according
to the regulations of the University that pertain to emeritus faculty members [1].
1.2.2 The Voting Faculty
1.2.2.1 The voting faculty in the election of Department representatives to School and University
academic governance bodies shall be the regular faculty.
1.2.2.2 The voting members in Department Meetings, committee meetings, and in the election
of representatives to Department academic governance bodies shall be the members who are
regular faculty or are other members to whom the regular faculty of the Department have voted
to extend voting privileges; the statement of a voting privilege of another member shall specify
the meeting(s) or election(s) to which the privilege pertains.
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1.2.2.3 A tenure-track or tenured faculty member may be elected to an academic governance
body of the Department or School only if that individual is qualified to vote in the election of
representatives to that body.
1.3 THE SPECIALISTS
A Specialist shall be any person who holds a full-time, nonacademic appointment in the
Department.
1.4 THE ADMINISTRATION
The Administration of the Department shall consist of the Chair of the Department, the Acting
Chair, the Specialists, the Chair of the Graduate Program Committee and the Chair of the
Undergraduate Program Committee.
2 MEETINGS
2.1 PROCEDURES
2.1.1 The conduct of all meetings of standing committees and of all meetings of the Department
shall be governed by Robert's Rules of Order, as revised, except as these Bylaws specify.
2.1.2 Individuals other than members of the Department may attend meetings of the
Department and of its committees with the permission of the members. The person presiding at
the meeting may grant such individuals the right to participate in the discussion.
2.2. MEETINGS OF THE DEPARTMENT
2.2.1 It is through these meetings that the regular faculty shall exercise its responsibilities and
that the Chair of the Department shall inform the faculty about the formal recommendations of
the Faculty Advisory Council and about the major administrative events and policies, such as
activities of the Dean's Chairs' Council.
2.2.2 The persons eligible to vote shall be the voting members of the Department (defined in
Section 1.2.2.2).
2.2.3 The Department Chair shall convene a meeting of the Department at least once each
academic session, except for summer sessions, and shall preside at the meetings of the
Department.
2.2.4 A majority of the voting members of the Department shall constitute a quorum.
2.2.5 Consideration of matters of official business shall require that a quorum be present.
Approval of such business shall require a favorable vote by a majority of those voting. Business
of the Department requiring a vote shall be listed on the Agenda, which shall be distributed to
the members of the Department at least two days before the date of the meeting.
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2.2.6 The voting faculty shall select a Secretary to record minutes of all meetings, distribute
them to the members of the Department and maintain them in a permanent archive.
3. ORGANIZATION OF THE DEPARTMENT
3.1. ADMINISTRATION
3.1.1 CHAIR OF THE DEPARTMENT
3.1.1.1 The Chair of the Department shall participate in the academic governance and
administration of the Department and University, as established in these Bylaws, the Bylaws of
the School and the Indiana University Handbooks [1,2]. Specifically, the Chair and the faculty of
the Department share responsibility in the areas II.3d-II.3p listed in the Bylaws of the School
(1997) [3].
3.1.1.2 The Chair of the Department shall be a tenured member of the regular faculty.
3.1.1.3 Nomination of Candidates for the Chair of the Department
The regular faculty shall share responsibility with the Dean of the School for determining the
procedures for selecting the nominee for the Chair.
3.1.1.4 Specific Responsibilities
3.1.1.4.1 The Chair is the chief administrative officer of the Department.
3.1.1.4.2 The Chair is responsible for the proper functioning of the educational, research and
service programs of the Department. This responsibility shall take into account the process of
academic governance of the Department. It includes, but is not limited to, scheduling,
budgetary matters, physical facilities and the personnel matters not under the jurisdiction of the
Primary Committee.
3.1.1.4.3 Near the end of each fiscal year, the Chair shall consult the Faculty Advisory Council
and then select the Acting Chair for the following year.
3.1.1.4.4 The Chair shall meet with the Faculty Advisory Council at least once each Fall and
Spring sessions concerning matters of shared responsibility. The Chair shall actively solicit the
advice of the Faculty Advisory Council on matters listed in Item 3.2.1.2.2 of these Bylaws and
shall carefully consider its recommendations.
3.1.1.4.5. The Chair shall participate, along with the Primary Committee, in the process of
reappointment, promotion, tenure and dismissal of faculty members according to the procedures
established in [4].
3.1.1.4.6 The Chair shall review each member of the Department annually according to the
rules and regulations of the School and the University and shall inform the Department
members of the date and nature of the review at the beginning of each academic year.
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3.1.1.4.7 In consultation with the Faculty Advisory Council, the Chair shall establish, and review
annually, the criteria and procedures to be used in merit salary decisions. The Chair shall
communicate them to the faculty members prior to making committee assignments and reviews
and at any other times there are changes to them.
3.1.1.4.8 The Chair of the Department shall seek the advice of the Graduate Program
Committee on matters pertaining to the graduate program and shall present the formal
recommendations of that Committee to the graduate faculty for its recommendations.
3.1.1.4.9 The Chair shall seek the advice of the Undergraduate Program Committee on matters
of shared responsibility pertaining to the undergraduate program in accordance with [3].
3.1.1.4.10 The Chair shall seek the advice of the Infrastructure Committee on matters relating to
infrastructure and computing in the Department.
3.1.2. ACTING CHAIR OF THE DEPARTMENT
3.1.2.1 Near the end of each fiscal year, and at other times as necessary, the Department Chair
shall select, by mutual agreement between the Chair and the nominee, a member of the regular
faculty who is not a member of the Faculty Advisory Council, to serve as Acting Chair. Prior to
selection, the Chair shall consult the Advisory Council to determine the opinion of the faculty on
the matter.
3.1.2.2 The role of the Acting Chair is to act in place of the Chair in the Chair's absence from the
Department, except as the Chair may prescribe at any time. It is the joint responsibility of the
Chair and Acting Chair to assure the smooth transition between each other's administrations.
Other than this, the Acting Chair has no responsibilities when the Chair is in the Department. It
is expected that any recompense allocated to the position will be commensurate with the effort
expended, as mutually agreed upon by the Chair and the Acting Chair.
3.1.3 CHAIR OF THE GRADUATE PROGRAM COMMITTEE
3.1.3.1 The role of the Graduate Program Committee Chair shall be to supervise the academic
and administrative affairs of the Graduate Program as determined by the Chair of the
Department and the graduate faculty.
3.1.3.2 Early in the fall semester, and at other times as necessary, the Department Chair shall
consult the Faculty Advisory Council to determine the graduate faculty's preferences concerning
candidates for the position of Graduate Program Committee Chair. After this, the Department
Chair shall select a member of the graduate faculty to fill this position.
3.1.3.3 Specific responsibilities of the Graduate Program Committee Chair are:
3.1.3.3.1 to serve as a member and Chair of the Graduate Program Committee;
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3.1.3.3.2 to supervise the advising of all graduate students in accordance with the regulations
that the graduate faculty establishes with the approval of the Department Chair and with the
University's guidelines [8];
3.1.3.3.3 to coordinate the activities of the Graduate Advisors in accordance with the Purdue
University procedures established in [5];
3.1.3.3.4 to monitor all graduate applications, assuring that they are submitted in good order to
the Graduate Program Committee for admission recommendations;
3.1.3.3.5 to supervise the maintenance of contact with prospective graduate students regarding
the Graduate Program, application status, financial support and research opportunities;
3.1.3.3.6 to coordinate faculty efforts and to work with the Department Chair in the recruitment
of graduate students, including the development of materials and recruitment strategies;
3.1.3.3.7 to coordinate the activities of the Graduate Program Committee with those of the other
departmental committees;
3.1.3.3.8 to perform other activities as directed by the Department Chair.
3.1.4. CHAIR OF THE UNDERGRADUATE PROGRAM COMMITTEE
3.1.4.1 The role of the Undergraduate Program Committee Chair shall be to supervise the
academic and administrative affairs of the Undergraduate Program as determined by the Chair
of the Department and the voting faculty.
3.1.4.2 Early in the fall semester, and at other times as necessary, the Department Chair shall
consult the Faculty Advisory Council to determine the voting faculty's preferences concerning
candidates for the position of Undergraduate Program Committee Chair. After this, the
Department Chair shall select a member of the regular faculty to fill this position.
3.1.4.3 Specific responsibilities of the Undergraduate Program Committee Chair are:
3.1.4.3.1 to serve as a member and Chair of the Undergraduate Program Committee and to
present the formal recommendations of that Committee to the faculty;
3.1.4.3.2 to supervise the advising of all undergraduate students in accordance with the
regulations that the voting faculty establishes and with the University's guidelines [8];
3.1.4.3.3 to supervise or delegate the monitoring of all undergraduate applications, assuring that
they are submitted in good order to the Undergraduate Program Committee for admission
decisions;
3.1.4.3.4 to supervise or delegate the maintenance of contact with prospective undergraduate
students regarding the Undergraduate Program, application status, financial support and
research opportunities;
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3.1.4.3.5 to coordinate faculty efforts and to work with the Department Chair in the recruitment
of undergraduate students, including the development of materials and recruitment strategies;
3.1.4.3.6 to coordinate the activities of the Undergraduate Program Committee with those of the
other departmental committees;
3.1.4.3.7 to perform other activities as directed by the Department Chair and the voting faculty.
3.2 THE STANDING COMMITTEES
3.2.1 FACULTY ADVISORY COUNCIL
3.2.1.1. Membership and Election
3.2.1.1.1 The Faculty Advisory Council shall consist of three members of the regular faculty,
excluding the Chair and the Acting Chair.
3.2.1.1.2 The term of office of the members shall be two years except that, initially, one member
shall serve for one year.
3.2.1.1.3 New members shall be elected during the Spring session and shall take office at the
beginning of the Fall session.
3.2.1.1.4 A member with an unexpired term may not stand for election to an overlapping term.
3.2.1.1.5 The members of the Faculty Advisory Council are elected in a secret ballot by the
voting faculty. If more than one seat on the Council is vacant, the candidates with the most
votes in the ballot shall fill the vacancies. Repeated balloting shall be used for any ties that
need to be resolved. Vacancies occurring for less than a full term, other than for summer
sessions, shall be filled for the remainder of the term by election in this way, too.
3.2.1.1.6 At the first meeting at which a new member elected to a full term takes office, the
Council shall elect a President by majority vote of the Council members. The role of the
President of the Council is to preside at the Council meetings and act as Council spokesperson.
3.2.1.1.7 The Advisory Council shall select a Secretary to record minutes of all meetings and
distribute them to the faculty of the Department.
3.2.1.2 Function
3.2.1.2.1 The role of the Faculty Advisory Council is to serve as a vehicle of communication
among the faculty, specialists, students, and the Chair of the Department.
3.2.1.2.2 The Advisory Council shall advise the Chair of the Department on matters of shared
responsibility according to the Bylaws of the School [3]. These shall include, but not be limited
to, the following specific matters:
a) General policy recommendations pertaining to the educational and research programs of
the Department;
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b) Assignments to all committees;
c) Policies pertaining to faculty loads;
d) Policies relating to faculty compensation;
e) Policies on recommending faculty for re-appointment, tenure, and promotion;
f) Selection of individuals for the chairs of the standing committees and the Acting Chair.
3.2.1.2.3 The Advisory Council shall act as the Departmental Grievance Committee in
accordance with the grievance procedures of the University, including those of students [6]. It
shall also act as an informal grievance sounding board for members of the Department and
students.
3.2.1.3 Meetings
3.2.1.3.1 The Faculty Advisory Council shall meet with the Department Chair at least once each
Fall and Spring sessions.
3.2.1.3.2 The Advisory Council shall meet without the Department Chair at the call of any
member. In this case, the President need not notify the Department Chair about the meeting.
Additional meetings shall be held at the call of the Department Chair, any member of the
Advisory Council, or by petition of at least 20% of the voting faculty.
3.2.2 PRIMARY COMMITTEE
3.2.2.1 This committee participates, with the Chair of the Department, in the process of
recommending reappointment, promotion and tenure of the regular faculty according to the
University's procedures [1-3] and [4] of Purdue University.
3.2.2.2 It shall also serve as the responsible committee in faculty disciplinary and dismissal
cases and other matters as the Chair of the Department or the voting faculty delegate.
3.2.2.3 The departmental Promotion and Tenure guidelines and changes thereof shall be voted
on by all voting members of the faculty.
3.2.2.4 The departmental representative to the Unit Committee, unless decided by the Dean of
the School, shall be elected by the voting faculty for each academic year.
3.2.3 GRADUATE PROGRAM COMMITTEE
3.2.3.1 The Graduate Program Committee shall consist of members of the graduate faculty
named by the Department Chair with the advice of the Faculty Advisory Council.
3.2.3.2 The Chair shall constitute the Committee at the time of naming the Graduate Program
Chair, who shall be a voting member.
107
3.2.3.3. Function
3.2.3.3.1 The Committee shall advise, and recommend to, the Department Chair concerning
matters relating to the Graduate Program in the Department that are shared responsibility
according to the Bylaws of the School [3], the program authorization of the Dean of the
Graduate School of Purdue University [7], as revised, and the Purdue University Policies
Manual [5]. This shall include specifically, but not be limited to, the following:
a) establishing academic standards, course content and degree requirements for all
graduate programs;
b) scheduling graduate course offerings, including the courses, the number of sections, and
the frequency of course offerings;
c) admission and retention standards for all graduate programs;
d) policies on financial support of teaching assistantships and fellowships;
e) evaluation and selection of nominees to be recommended for admission to the Graduate
Program;
f) examination of candidates to the graduate degree in compliance with [5].
3.2.3.3.2. The Committee shall exercise the authority in all matters relating to the department's
graduate educational and research programs that the Department Chair and the graduate
faculty delegate to it.
3.2.3.3.3 The Chair of the Department shall inform the graduate faculty of the formal
recommendations of the Committee on matters of graduate faculty responsibility pertaining to
the Graduate Program pursuant to 3.2.3.3.1.
3.2.3.4 The Chair of the Committee shall call meetings at least once each session, excluding
summer sessions, and as necessary to conduct its business.
3.2.4 UNDERGRADUATE PROGRAM COMMITTEE
3.2.4.1. Membership
3.2.4.1.1 The Undergraduate Program Committee shall consist of members of the regular
faculty and others named by the Department Chair with the advice of the Faculty Advisory
Council.
3.2.4.1.2 The Department Chair shall constitute the Committee at the time of naming the
Undergraduate Program Chair, who shall be a voting member.
3.2.4.2 Function
108
3.2.4.2.1 The Committee shall advise the faculty on matters relating to the undergraduate
educational and research programs that are the responsibility of the faculty according to the
Bylaws of the School [3].
These shall include, but not be limited to:
a) admission and retention standards;
b) establishment and maintenance of the specific curriculum structure and academic
requirements for the undergraduate major and service course programs;
c) creation and dropping of courses; and
d) content and prerequisites of new and existing courses.
3.2.4.2.2 The Committee shall advise the Department Chair on matters relating to the
undergraduate educational and research programs that are the shared responsibility of the
faculty and Department Chair according to the Bylaws of the School [3]. These shall include,
but not be limited to
a) scheduling of courses and class size;
b) policies concerning part-time instructors and undergraduate student assistants.
3.2.4.2.3 The Committee shall exercise the authority in matters relating to the undergraduate
programs in the Department that the faculty and the Department Chair delegate to it.
3.2.4.2.4 The Chair of the Committee shall present to the faculty the formal recommendations of
the Undergraduate Program Committee on matters of faculty responsibility, shared or not,
pertaining to the undergraduate programs in accordance with [3].
3.2.4.3 The Chair of the Committee shall call meetings at least once each session, excluding
summer sessions, and as necessary to conduct its business.
3.2.5 INFRASTRUCTURE COMMITTEE
3.2.5.1 Membership and Appointment
3.2.5.1.1 Early in the Fall session, the Department Chair shall select all of the members of the
Infrastructure Committee from among the members of the regular faculty and specialists of the
department with the advice of the Faculty Advisory Council.
3.2.5.1.2 The Department Chair shall select the Chair of the Infrastructure Committee from
among the regular faculty members of the Committee.
3.2.5.2 Function
109
3.2.5.2.1 The Committee shall advise the department members and the Department Chair on
matters concerning research, educational and administrative computing in the Department,
including hardware, software, and space issues.
3.2.5.2.2 The Committee shall advise the Department Chair on the specific matters:
a) Policies for the procurement, use, and maintenance of hardware and software for the
instructional programs of the Department;
b) Purchase of specific hardware and software for the instructional programs of the
Department and for the use of the full, part-time and student faculty and the specialists;
c) Expenditure of the Department's budget for infrastructure items;
d) Coordination of the common equipment for the research programs of the Department.
3.2.5.2.3 The committee shall perform other activities as directed by the Department Chair or
the faculty.
3.2.5.2.4 The Chair of the Committee shall report the formal recommendations of the committee
to the members of the Department at the Department's meetings.
3.2.5.2.5 The Chair of the Committee shall call meetings of the committee at least once every
academic session, except summer sessions, and as necessary to conduct its business.
4 REPRESENTATIVE TO THE STEERING COMMITTEE OF THE
SCHOOL'S FACULTY ASSEMBLY
4.1 The Faculty Representative to the Steering Committee shall be a tenured member of the
regular faculty.
4.2 Election of Representative
The regular faculty of the Department shall elect annually the Representative, in conformity with
the Bylaws of the School [3], by a favorable majority in a secret ballot of the regular faculty of
the Department. The regular faculty shall determine the balloting procedures. In case there is no
candidate for the position, the Department Chair shall appoint a representative from the faculty
members eligible according to 4.1.
5 STUDENT ADVISING
The Department adopts the recommendations concerning goals and responsibilities in
administering and carrying out the process of academic advising described in [8].
6 ADJUDICATION OF RIGHTS AND RESPONSIBILITIES
FACULTY AND STUDENTS
110
Grievance procedures are in accordance with those defined by the University [1], [2], including
[1], [6] specifically for students.
7 BYLAWS PRESERVATION/DISSEMINATION PROCEDURES
7.1 These bylaws shall be made available upon request.
8 BYLAWS AMENDMENT PROCEDURES
8.1 Approval of amendments to these Bylaws shall be a shared responsibility of the voting
faculty and Chair of the Department. A favorable vote by greater than or equal to two thirds
majority of the voting faculty is required for approval, with the Chair of the Department
possessing line-item veto for items or clauses that pertain to the administrative responsibilities
having budgetary consequences in areas that are shared responsibility according to the Bylaws
of the School [3].
8.2 The Department Chair, the Faculty Advisory Council or any two members of the regular
faculty may propose amendments to these Bylaws by presenting them at a meeting of the
department.
8.3 These Bylaws shall be amended in a secret ballot, and the amendments shall take effect
immediately after approval.
8.4 With any change of the Bylaws, The Department Chair shall prepare, archive and transmit
updated copies of the Bylaws to the department members and to the Dean of the School. A
department member may request a copy of the Bylaws at any time.
8.5 The voting faculty shall review the Bylaws for possible amendment at regular intervals not to
exceed five years.
REFERENCE DOCUMENTS
[1] Academic Handbook. Indiana University, Bloomington, Indiana.
[2] Indiana University Academic Handbook: IUPUI Supplement. Indiana University,
Indianapolis, Indiana.
[3] Bylaws of the Faculty Assembly. Purdue University School of Science, IUPUI,
Indianapolis, Indiana.
[4] The Purdue University School of Science at Indianapolis Criteria and Documentation
Guidelines for Promotion, Tenure and Reappointment. Purdue University School of
Science, Indianapolis, Indiana. 1995.
[5] Policies and Procedures Manual for Administering Graduate Student Programs. Purdue
University Graduate School. Purdue University, West Lafayette, Indiana.
[6] Code of Students' Rights, Responsibilities and Conduct. IUPUI, Indianapolis, Indiana.
[7] Recommendation Letter Concerning Administration of the Master's Program in
Computer & Information Science at IUPUI to Dr. Luis Proenza, Dean of the Graduate
111
School from Dr. Ahmed Sameh, Head of the Computer Science Graduate Program.
Purdue University. April 28, 1997.
[8] Academic Advising in the School of Science. School of Science, IUPUI, Indianapolis,
Indiana. 1997.
112
Ethnic Minority Enrollment Data Tables
Race/Ethnicity Fall 2011
Spring 2012
Fall 2012
Spring 2013
Fall 2013
Spring 2014
Fall 2014
American Indian/Alaska Native 2 2 2 2 2 1 2
Asian 14 15 27 23 34 41 40
Black/African American 8 9 8 9 13 14 12
Hispanic/Latino 5 4 6 6 9 10 10
Native Hawaiian/Other Pacific Islander 0 0 0 0 0 0 0
N/A 16 16 14 18 14 15 16
White 106 108 156 147 146 158 187
Total 151 154 213 205 218 239 267
Undergraduate enrollment by Race/Ethnicity
Race/Ethnicity Fall 2011
Spring 2012
Fall 2012
Spring 2013
Fall 2013
Spring 2014
Fall 2014
American Indian/Alaska Native 0 0 1 0 0 0 0
Asian 71 72 105 113 133 126 125
Black/African American 2 0 4 4 1 1 3
Hispanic/Latino 2 2 1 2 2 2 2
Native Hawaiian/Other Pacific Islander 1 1 1 0 0 1 1
N/A 4 3 6 5 4 3 4
White 26 20 29 21 30 28 25
Total 106 98 147 145 170 161 160
Graduate enrollment by Race/Ethnicity
113
Undergraduate Student Learning Outcomes The Department’s Undergraduate Committee states the following Student Learning Outcomes. After graduation, a student should be able to:
1) Write software programs in multiple programming languages 2) Understand the theoretical foundations of computer science, including the study of
discrete computational structures 3) Understand and use different programming language paradigms such as procedural,
object-oriented, etc. 4) Use different data structures such as linked lists, arrays, stacks, trees, graphs, hash
tables, etc. to improve efficiency of software, and mathematically or experimentally analyze them and operations on them.
5) Know a diverse array of computational algorithms and their analysis techniques, as related to searching, sorting, optimization, and graph problems.
6) Know fundamental limitations of designing efficient algorithms and the theoretical meaning of the P?=NP problem
7) Know the basic concepts in formal language theory and their application to compiler design
8) Understand the basic design of computer architecture and their relationship to software design
9) Understand and design the basic functionalities of different computer operating systems 10) Acquire knowledge in multiple advanced areas of computer science, such as databases,
data mining, multimedia, graphics, computing security, networking, software engineering, bio-computing, etc.
11) Design, develop, and test small scale software projects 12) Write scientific project reports and software documentation
114
Graduate Student Learning Outcomes Graduate Certificates (5)
1. Demonstrate a sound understanding of computing principles in the chosen area of study
(Biocomputing, Biometrics, Computer Security, Databases and Data Mining, Software
Engineering)
a. As evident from appropriate grades earned to satisfy the core course
requirement for a specific certificate program
2. Demonstrate an ability to work in a group
a. As evident from successfully developing moderately intense collaborative
projects (e.g., semester projects in courses)
3. Demonstrate an ability to solve moderately complex problems in the chosen area of
study
a. As evident from successful completion of elective courses in Computer Science
or related fields, as required by the Certificate program(s)
MS Students
1. Demonstrate a sound understanding of general fundamental computing concepts (e.g.,
algorithms, programming languages, operating systems, etc.)
a. As evident from appropriate grades earned to satisfy the core course
requirements
2. Demonstrate a relatively in-depth understanding of a subarea
a. As evident from successfully completing a series of courses in a sub-area (e.g.,
databases)
3. Demonstrate an ability to successfully work in a group and/or demonstrate an ability to
successfully carry out moderately complex software projects
a. As evident from successfully developing moderately intense collaborative
projects (e.g., semester projects in courses) and/or
b. As evident from software development assignments/projects in courses (e.g.,
projects in networking course)
Additional Expectation from MS Students choosing Thesis or Project Option
1. Demonstrate an ability to systematically carry out scientific research (empirical and/or
theoretical) on a moderately complex problem
Additional Expectation from PhD Students
1. Demonstrate an ability to develop original solutions and their validation that extend the
state-of-art in a chosen specialization to significant research problem(s) as evident from
publications in highly-ranked conferences/journals
115
Undergraduate Principles of Undergraduate Learning (PUL) Assessment—Spring 2010 through Fall 2013
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Major Emphasis (100 Level & Lower)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1C. Information Resource Skills 35 4 12 0 19 35
2.97 11.4 34.3 0.0 54.3 100.0
Total 1
35 4 12 0 19 35
2.97 11.4 34.3 0.0 54.3 100.0 1 Combined number of student ratings in all 100-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 100 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Moderate Emphasis (100 Level & Lower)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
5. Understanding Society and Culture 35 11 3 5 16 35
2.74 31.4 8.6 14.3 45.7 100.0
Total 1
35 11 3 5 16 35
2.74 31.4 8.6 14.3 45.7 100.0 1 Combined number of student ratings in all 100-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013.. A student
may be evaluated more than once if he or she is taking more than one 100 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
116
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Major Emphasis (200 Level)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
2. Critical Thinking 105 16 3 14 72 105
3.35 15.2 2.9 13.3 68.6 100.0
Total 1
105 16 3 14 72 105
3.35 15.2 2.9 13.3 68.6 100.0 1 Combined number of student ratings in all 200-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 200 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Moderate Emphasis (200 Level)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1B. Quantitative Skills 105 16 6 13 70 105
3.30 15.2 5.7 12.4 66.7 100.0
Total 1
105 16 6 13 70 105
3.30 15.2 5.7 12.4 66.7 100.0 1 Combined number of student ratings in all 200-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 200 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
117
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Major Emphasis (300 Level)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
2. Critical Thinking 72 23 18 13 18 72
2.36 31.9 25.0 18.1 25.0 100.0
3. Integration and Application of Knowledge 6 0 2 2 2 6
3.00 0.0 33.3 33.3 33.3 100.0
Total 1
78 23 20 15 20 78
2.41 29.5 25.6 19.2 25.6 100.0 1 Combined number of student ratings in all 300-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 300 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Moderate Emphasis (300 Level)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1B. Quantitative Skills 72 19 18 14 21 72
2.51 26.4 25.0 19.4 29.2 100.0
Total 1
72 19 18 14 21 72
2.51 26.4 25.0 19.4 29.2 100.0 1 Combined number of student ratings in all 300-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 300 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
118
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Major
Emphasis (400 Level)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1A. Written, Oral, & Visual Communication Skills 1 0 0 1 0 1
3.00 0.0 0.0 100.0 0.0 100.0
1B. Quantitative Skills 39 5 11 21 2 39
2.51 12.8 28.2 53.9 5.1 100.0
2. Critical Thinking 167 20 38 78 31 167
2.72 12.0 22.8 46.7 18.6 100.0
3. Integration and Application of Knowledge 1 0 0 0 1 1
4.00 0.0 0.0 0.0 100.0 100.0
4. Intellectual Depth, Breadth, and Adaptiveness 1 0 0 0 1 1
4.00 0.0 0.0 0.0 100.0 100.0
Total 1
209 25 49 100 35 209
2.69 12.0 23.4 47.8 16.7 100.0 1 Combined number of student ratings in all 400-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 400 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
119
Faculty Ratings of Department of Computer Science: Major Courses Student Performance on PULs with Moderate Emphasis (400 Level)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1B. Quantitative Skills 160 19 44 74 23 160
2.63 11.9 27.5 46.3 14.4 100.0
2. Critical Thinking 40 4 20 11 5 40
2.43 10.0 50.0 27.5 12.5 100.0
3. Integration and Application of Knowledge 1 0 0 0 1 1
4.00 0.0 0.0 0.0 100.0 100.0
6. Values and Ethics 8 1 1 2 4 8
3.13 12.5 12.5 25.0 50.0 100.0
Total 1
209 24 65 87 33 209
2.62 11.5 31.1 41.6 15.8 100.0 1 Combined number of student ratings in all 400-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 400 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Major Emphasis (100 Level & Lower)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1C. Information Resource Skills 108 11 13 48 36 108
3.01 10.2 12.0 44.4 33.3 100.0
Total 1
108 11 13 48 36 108
3.01 10.2 12.0 44.4 33.3 100.0 1 Combined number of student ratings in all 100-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 100 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
120
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Moderate Emphasis (100 Level & Lower)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
2. Critical Thinking 108 12 13 53 30 108
2.94 11.1 12.0 49.1 27.8 100.0
Total 1
108 12 13 53 30 108
2.94 11.1 12.0 49.1 27.8 100.0 1 Combined number of student ratings in all 100-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013.. A student
may be evaluated more than once if he or she is taking more than one 100 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Major Emphasis (200 Level)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1B. Quantitative Skills 66 6 2 15 43 66
3.44 9.1 3.0 22.7 65.2 100.0
2. Critical Thinking 439 63 38 43 295 439
3.30 14.4 8.7 9.8 67.2 100.0
Total 1
505 69 40 58 338 505
3.32 13.7 7.9 11.5 66.9 100.0 1 Combined number of student ratings in all 200-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 200 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
121
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Moderate Emphasis (200 Level)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1A. Written, Oral, & Visual Communication Skills 23 1 0 0 22 23
3.87 4.3 0.00 0.00 95.7 100.0
1C. Information Resource Skills 385 52 38 48 247 385
3.27 13.5 9.9 12.5 64.2 100.0
3. Integration and Application of Knowledge 97 13 11 14 59 97
3.23 13.4 11.3 14.4 60.8 100.0
Total 1
505 66 49 62 328 505
3.29 13.1 9.7 12.3 65.0 100.0 1 Combined number of student ratings in all 200-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 200 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Major Emphasis (300 Level)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1A. Written, Oral, & Visual Communication Skills 61 4 5 19 33 61
3.33 6.6 8.2 31.1 54.1 100.0
1B. Quantitative Skills 27 1 0 1 25 27
3.85 3.7 0.0 3.7 92.6 100.0
2. Critical Thinking 617 62 92 105 358 617
3.23 10.1 14.9 17.0 58.0 100.0
Total 1
705 67 97 125 416 705
3.26 9.5 13.8 17.7 59.0 100.0 1 Combined number of student ratings in all 300-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 300 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
122
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Moderate Emphasis (300 Level)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1A. Written, Oral, & Visual Communication Skills 66 4 11 15 36 66
3.26 6.1 16.7 22.7 54.6 100.0
1B. Quantitative Skills 278 18 59 63 138 278
3.15 6.5 21.2 22.7 49.6 100.0
1C. Information Resource Skills 275 36 28 28 183 275
3.30 13.1 10.2 10.2 66.5 100.0
3. Integration and Application of Knowledge 61 3 3 20 35 61
3.43 4.9 4.9 32.8 57.4 100.0
6. Values and Ethics 25 2 5 0 18 25
3.36 8.0 20.0 0.0 72.0 100.0
Total 1
705 63 106 126 410 705
3.25 8.9 15.0 17.9 58.2 100.0 1 Combined number of student ratings in all 300-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 300 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
123
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Major
Emphasis (400 Level)
PUL – Major Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
1A. Written, Oral, & Visual Communication Skills 16 1 1 2 12 16
3.56 6.3 6.3 12.5 75.0 100.0
1B. Quantitative Skills 3 0 3 0 0 3
2.00 0.0 100.0 0.0 0.0 100.0
6. Values and Ethics 5 0 0 1 4 5
3.80 0.0 0.0 20.0 80.0 100.0
Total 1
24 1 4 3 16 24
3.42 4.2 16.7 12.5 66.7 100.0 1 Combined number of student ratings in all 400-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 400 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
Faculty Ratings of Department of Computer Science: Non-Major Courses Student Performance on PULs with Moderate Emphasis (400 Level)
PUL – Moderate Emphasis Mean 2
Not
Effective
Somewhat
Effective
Effective
Very
Effective
Total
3. Integration and Application of Knowledge 19 1 5 1 12 19
3.26 5.3 26.3 5.3 63.2 100.0
4. Intellectual Depth, Breadth, and Adaptiveness 5 0 0 1 4 5
3.80 0.0 0.0 20.0 80.0 100.0
Total 1
24 1 5 2 16 24
3.38 4.2 20.8 8.3 66.7 100.0 1 Combined number of student ratings in all 400-level courses sampled in Spring 2010, Fall 2010, Spring 2011, Fall 2011, Spring 2012, Fall 2012, Spring 2013 and Fall 2013. A student
may be evaluated more than once if he or she is taking more than one 400 level course. 2
Scale: 1 = “Not Effective”, 2 = “Somewhat Effective”, 3 = “Effective”, 4 = “Very Effective”
124
Listing of Publications and Grants
This list contains publications and awards by research group; as the Department faculty work in close
collaboration with each other and with other groups, some publications may be repeated in the listing in
more than one group if they are co-authored, for example. IUPUI CS Graduate student co-authors are
indicated with *.
Database, Data Mining and Machine Learning (DDMML) Group
Referred Publications
1. *Mahmudur Rahman, *Mansurul Alam Bhuiyan, Mohammad Al Hasan, “GRAFT: An Efficient Graphlet Counting Mehtod for Large Graph Analysis”, In IEEE Transaction on Knowledge Discovery and Data Mining, DOI: 10.1109/TKDE.2013.2297929, 2014
2. *Mahmudur Rahman, *Mansurul Alam Bhuiyan, Mahmuda Rahman, Mohammad Al Hasan, “GUISE: a uniform sampler for constructing frequency histogram of graphlets”, Knowledge and Information Systems, 38 (3): 511-536 (2014)
3. *Baichuan Zhang, *Tanay Kumar Saha, Mohammad Al Hasan, “Name Disambiguation from link data in a collaboration graph”, In Proc. of IEEE/ACM Conference on Social Network Analysis and Mining, 2014
4. *Mansurul Alam Bhuiyan, Mohammad Al Hasan, “FSM-H: Frequent Subgraph Mining Algorithm in Hadoop”, In Proc. of 3rd International Congress of BigData, 2014
5. Nish Parikh, Prasad Sriram, Mohammad Al Hasan, “On Segmentation of eCommerce Queries”, In Proceedings of ACM International Conference on Information and Knowledge Management, page 1137-1146, 2013
6. *Mansurul Alam Bhuiyan, Snehasis Mukhopadhyay, and Mohammad Al Hasan, “Interactive Pattern Mining on Hidden Data: A Sampling based Solution”, in Proceedings of ACM International Conference on Information and Knowledge Management, 95-104, 2012
7. James C Costello et al., "A community effort to assess and improve drug sensitivity prediction algorithms," Nature Biotechnology, June 2014.
8. Atul K Singh, Amanda M Bettasso, Euiwon Bae, Bartek Rajwa, Murat Dundar, Mark D Forster, Lixia Liu, Brent Barrett, Judith Lovchik, J Paul Robinson, E Daniel Hirleman, Arun K Bhunia, "Laser Optical Sensor, a Label-Free On-Plate Salmonella enterica Colony Detection Tool," mBio 5(1), 2014.
9. Murat Dundar, Bartek Rajwa, Lin Li, “Partially-observed Models for Classifying Minerals on Mars,” In Proceedings of WHISPERS'13, Gainesville, FL, June 25-28, 2013.
10. Mathew Palakal, Shiaofen Fang, Yuni Xia, *Anand Krishnan, *Sam Bloomquist, *Thanh Nguyen, Roland Gamache and Shaun Grannis, Detecting Comorbidity of Chlamydia from Clinical Reports for Health Terrain Visualization, 2013 Workshop on Visual Analytics in Healthcare, In conjunction with AMIA 2013.
11. *Jeremy Keiper, Yuni Xia, Shiaofen Fang, Mathew Palakal, Shaun Grannis, Roland Gamache, *Thanh Minh Nguyen, *Sam Bloomquist and *Anand Krishnan, Use Cases for Public Health Data Visualization, 2013 Workshop on Visual Analytics in Healthcare, In conjunction with AMIA 2013.
12. Yuni Xia, Shiaofen Fang, Mathew Palakal, Roland Gamache Jr, *Thanh Minh Nguyen, *Sam Bloomquist, *Anand Krishnan, *Jeremy Keiper, Shaun Grannis, Data Exploration of a Notifiable Condition Detector System, 2013 Workshop on Visual Analytics in Healthcare, In conjunction with AMIA 2013.
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13. Biao Qin, Yuni Xia, Fang Li and *Jiaqi Ge, EMU: An expectation maximization based approach for clustering uncertain data, Journal of Intelligent & Fuzzy Systems, 1067- 1083, 2013.
14. *Chandima Hewa Nadungodage, Yuni Xia, John Lee, Myungcheol Lee, Choon Seo Park, GPU Accelerated Item-Based Collaborative Filtering for Big-Data Applications, proceedings of the IEEE International Conference on Big Data (IEEE BigData) 2013.
15. Brian E. Dixon, Marc B. Rosenman, Yuni Xia, Shaun J. Grannis, A Vision for the Systematic Monitoring and Improvement of the Quality of Electronic Health Data. MedInfo 2013: 884-888.
16. *Chandima Hewa Nadungodage, Jaehwan John Lee, Yuni Xia, Miyoung Lee, Myungcheol Lee, GPU-based Memory Efficient Recommendation System for Big Data Applications, Poster, the International Conference on GPU technology, 2013.
17. *Chandima Hewa Nadungodage, Yuni Xia, Jaehwan John Lee, Yi-cheng Tu, Hyper-Structure Mining of Frequent Patterns in Uncertain Data Streams, Journal of Knowledge and Information Systems ( KAIS) , 2013.
18. Shaun Grannis, Brian Dixon, Yuni Xia, Jianmin Wu, Using Information Entropy to Monitor Chief Complaint Characteristics and Quality, International Society for Disease Surveillance Conference, 2012.
19. *Ferit Akova, Yuan Qi, Bartek Rajwa, Murat Dundar, “Self-adjusting Models for Semi-supervised Learning in Partially-observed Settings,” In Proceedings of the IEEE International Conference on Data Mining (ICDM’12), Brussels, Belgium, December 10-13, 2012.
20. Murat Dundar, *Ferit Akova, Yuan Qi, Bartek Rajwa, “Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes,” In John Langford and Joelle Pineau (Eds.), Proceedings of the 29th International Conference on Machine Learning (ICML'12), Edinburgh, Scotland, June 26-July 1, 2012 (pp. 113-120). Omnipress, 2012.
21. *Chandima H. Nadungodage, Yuni Xia, Pranav S. Vaidya, Yu Chen, Jaehwan Lee, Online Multidimensional Regression Analysis on Concept-drifting Data Streams, International Journal of Data Mining, Modeling and Management (IJDMMM).
22. Biao Qin, Yuni Xia, Shan Wang, Xiaoyong Du, A Novel Bayesian Classification for Uncertain Data, Knowledge-Based Systems, Knowledge-Based Systems, Volume 24, Issue 8, 1151-1158 , 2011.
23. J. Paul Robinson, Bartek P. Rajwa, Euiwon Bae, Valery Patsekin, Ali Roumani, Arun K. Bhunia, J. Eric Dietz, V. Jo Davisson, Murat Dundar, John Thomas, and E. Daniel Hirleman, "Using Scattering to Identify Bacterial Pathogens," Optics & Photonics News 22(10), 20-27, 2011.
24. J. Paul Robinson, Bartek P. Rajwa, Murat Dundar, Euiwon Bae, Valery Patsekin, E. Daniel Hirleman, Ali Roumani, Arun K. Bhunia, J. Eric Dietz, V. Jo Davisson, John G. Thomas, "A distributed national network for label-free rapid identification of emerging pathogens", Proceedings of SPIE, 8018, June 2011.
25. Bartek Rajwa, Murat Dundar, *Ferit Akova, Valery Patsekin, Euiwon Bae, Yanjie Tang, J. Eric Dietz, E. Daniel Hirleman, J. Paul Robinson, Arun K. Bhunia, "Digital microbiology: detection and classification of unknown bacterial pathogens using a label-free laser light scatter-sensing system", Proceedings of SPIE,8029, May 2011.
26. Murat Dundar, Sunil Badve, Gokhan Bilgin, Vikas Raykar, Olcay Sertel, Metin N. Gurcan, “Computerized Classification of Intraductal Breast Lesions using Histopathological Images”, IEEE Transactions on Biomedical Engineering, 58(7):1977-1984, 2011.
27. Yicheng Tu, Shaoping Chen, Yuni Xia, Performance Analysis of A Dual-Tree Algorithm for Computing Spatial Distance Histograms, The VLDB Journal. 20(4):471-494, 2011.
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28. *Omkar Tilak, *Andrew Hoblitzell, Snehasis Mukhopadhyay, *Qian You, Shiaofen Fang, Yuni Xia, Joseph Bidwell, Multi-Level Text Mining for Bone Biology, Concurrency and Computation: Practice and Experience, 2011.
29. Yu Chen, Pranav Vaidya, Jaehwan John Lee, *Chandima Hewa Nadungodage, Yuni Xia, Renfa Li, Qiang Wu, A New Hardware/Software Partitioning Methodology Combining Search Space Smoothing and Discrete Particle Swarm Optimization, , International Conference on Engineering of Reconfigurable Systems and Algorithms (ERSA), 2011.
30. Sandeep Raghuram, Yuni Xia, *Jiaqi Ge, Mathew Palakal, Josette Jones, Dave Pecenka, Eric Tinsley, Jean Bandos, and Jerry Geesaman. AutoBayesian: Developing Bayesian Networks Based on Text Mining, Demo, International Conference on Database Systems for Advanced Applications (DASFAA) 2011.
31. Biao Qin, Yuni Xia, Rakesh Sathyesh, *Jiaqi Ge, Sunil Probhakar, DTU: Decision Tree for Uncertain Data, Demo, International Conference on Database Systems for Advanced Applications (DASFAA) 2011.
32. *Chandima Hewa Nadungodage, Yuni Xia, Fang Li, Jaehwan John Lee, *Jiaqi Ge, StreamFitter: A Real Time Linear Regression Analysis System for Continuous Data Streams, Demo, International Conference on Database Systems for Advanced Applications (DASFAA) 2011.
33. Biao Qin, Yuni Xia, Sunil Prabhakar, Rule induction for uncertain data, Knowledge and Information Systems, Knowledge and Information Systems - KAIS , vol. 24, no. 2, 2010 .
34. Pranav Vaidya, Yu Chen, Jaehwan John Lee, *Chandima Hewa Nadungodage, and Yuni Xia, ”A General Purpose FPGA Data Filter For Data Stream Processing”, International Conference on Engineering of Reconfigurable Systems and Algorithms (ERSA), pp. 247-250, 2010.
35. *Jiaqi Ge, Yuni Xia, A Discretization Algorithm for Uncertain Data, the 21st International Conference on Database and Expert Systems Applications (DEXA), 2010.
36. Bartek Rajwa, Murat Dundar, *Ferit Akova, Amanda Betasso, Valery Patsekin, E. Dan Hirleman, Arun K. Bhunia, J. Paul Robinson, “Discovering unknown: detection of emerging pathogens using label-free light scattering system,” Cytometry Part A, 77A(12):1103–1112, 2010.
37. *Ferit Akova, Murat Dundar, V. Jo Davisson, E. Daniel Hirleman, Arun K. Bhunia, J. Paul Robinson, Bartek Rajwa, “A Machine-learning Approach for Label-free Detection of Unmatched Bacterial Serovars”, Statistical Analysis and Data Mining Journal, 3(5):289-301, 2010
38. Murat Dundar, Sunil Badve, Vikas Raykar, Rohit Jain, Olcay Sertel, Metin Gurcan, “A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides”, In Proceedings of 20th International Conference on Pattern Recognition, Istanbul, Turkey, August 23-26, 2010 (pp. 2732-2735). (Best scientific paper in Biomedical and Bioinformatics applications
39. *Andrew Hoblitzell, Snehasis Mukhopadhyay, *Qian You, Shiaofen Fang, Yuni Xia, Joseph Bidwell, Text Mining for Bone Biology, Proceeding of the Workshop on Emerging Computational Methods for the Life Sciences, 2010.
40. Pranav S. Vaidya, Jaehwan John Lee, Francis Bowen, Yingzi Du, *Chadima H. Nadungodage, Yuni Xia. Symbiote - A Reconfigurable Logic Assisted Data Stream Management System, ACM SIGMOD Conference, Demo, 2010.
41. *Jiaqi Ge, Yuni Xia, *Chandima Hewa Nadungodage. Classify Uncertain Data with Neural Network, the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2010.
42. Biao Qin, Yuni Xia, Fang Li. A Bayesian Classifier for Uncertain Data. the 25th ACM Symposium on Applied Computing (SAC), 2010.
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43. Biao Qin, Yuni Xia, Rakesh Sathyesh, Sunil Prabhakar, Yicheng Tu, uRule: A Rule Based Classifier for Data with Uncertainty, the IEEE International Conference on Date Mining (ICDM), Demo, 2009.
44. Biao Qin, Yuni Xia, Fang Li. DTU: A Decision Tree for Uncertain Data. Proc. of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2009.
45. Biao Qin, Yuni Xia, Sunil Prabhakar, Yicheng Tu. A Rule-Based Classification Algorithm for Uncertain Data. Proc. of the IEEE workshop on Management and Mining Of Uncertain Data(MOUND), in conjunction with International Conference of Data Engineering, 2009.
46. *Andrew Campen, Yuni Xia, Dan Rigsby, Ying Guo, Xingdong Feng, Eric W. Su, Mathew Palakal and Shuyu Li. Mining Gene Expression Database for Primary Human Disease Tissues. Proc. of the IEEE 24th International Conference on Date Engineering (ICDE), Demo, 1604-1608, 2008.
47. Yuni Xia, Jonathan Munson, David Wood, Alan Cole. Location-based Service System (LBS) Analysis and Design. Handbook of Research on Modern Systems Analysis and Design Technologies and Applications, ISBN: 978-1-59904-887-1, Publisher: Information Science Reference, 2008.
48. Sunil Prabhakar, Dmitri V. Kalashnikov, and Yuni Xia. Query Indexing and Velocity Constrained Indexing. Encyclopedia of GIS, ISBN: 978-0-387-30858-6, Publisher: Springer Science, 2008.
49. Meeta Pradhan and Yuni Xia. Bioterrorism and Biosecurity. Handbook of Research on Information Security and Assurance, ISBN: 978-1-59904-855-0, Publisher: Information Science Reference, 2008.
50. Biao Qin, Yuni Xia. Generating Efficient Safe Query Plans for Probabilistic Databases. Journal of Data and Knowledge Engineering (DKE), Volume 67, Issue 3, 485-503, 2008.
51. Jiangang Liu, *Andrew Campen, Shuguang Huang, Sheng-Bin Peng, Xiang Ye, Mathew Palakal, A. Keith Dunker, Yuni Xia and Shuyu Li. Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data. BMC Medical Genomics, Volume 1, 39-52, 2008.
52. Yuni Xia, Sunil Prabhakar, Shan Lei, Reynold Cheng and Rahul Shah. Efficient Indexing for the Update Intensive Environment. International Journal of High Performance Computing and Networking, Volume 5, Issue 4, 263-272, 2008.
53. Yuni Xia, Bowei Xi. Conceptual Clustering Categorical Data with Uncertainty. Proc. of the IEEE 19th International Conference on Tools with Artificial Intelligence (ICTAI), 329-336, 2007.
54. Yuni Xia, *Andrew Campen, Dan Rigsby, Ying Guo, Xingdong Feng, Eric Su, Mathew Palakal, Shuyu Li. DGEM - a Microarray Gene Expression Database for Primary Human Disease Tissues. Molecular Diagnosis and Therapy, Volume 11, Issue 3, 145-149, 2007.
55. S. Mukhopadhyay and K. S. Narendra. "Adaptation and Learning in Decentralized Systems", (invited) Proceedings of the Yale Workshop on Adaptive and Learning Systems, New Haven, CT, June 2013.
56. *V. Singh, S. Mukhopadhyay, and M. Babbar-Sebens, "User Modeling for Interactive Optimization Using Neural Network", Proceedings of the IEEE Systems, Man, and Cybernetics (SMC) Conference, Oct 13-16, Manchester, UK, 2013.
57. C. Lapish., *N. Tirupattur, and S. Mukhopadhyay. "Text Mining for Neuroscience: A Co-morbidity Case Study." In Knowledge-Based Systems in Biomedicine and Computational Life Science, pp. 117-136. Springer Berlin Heidelberg, 2013. (invited book chapter)
58. *V. Singh, M. Babbar-Sebens, and S. Mukhopadhyay. "Decentralized Pursuit Learning Automata in Batch Mode", Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems and the 13th International Symposium on Advanced Intelligent Systems, November 20-24, 2012, Kobe, Japan
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59. *O. Tilak, M. Babbar-Sebens, and S. Mukhopadhyay. Decentralized and partially decentralized reinforcement learning for designing a distributed wetland system in watersheds. pp. 271 -- 276, Proceedings of the 2011 IEEE International Systems, Man, and Cybernetics (SMC) Conference, Anchorage, AK, October, 2011
60. S. Mukhopadhyay, C. Lapish, and *N. Turupattur. Analysis of Comorbidity of Neurological Disorders Using Text Mining: A Case Study. pp. 7 -- 16, (Invited) Proceedings of the 15th Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, June 2011.
61. *O. Tilak and S. Mukhopadhyay. Partially decentralized reinforcement learning in finite, multi-agent Markov decision processes. pp. 293-309, vol. 24, no. 4, AI Communications Journal, IOS Press, December 2011.
62. *N. Tirupattur, C. Lapish, and S. Mukhopadhyay. Text Mining for Neuroscience. Volume 1371, pp. 118-127, Proceedings of the 2011 International Symposium on Computational Models for Life Sciences (CMLS-11), AIP Press, June 2011.
63. *O. Tilak, R. Martin, and S. Mukhopadhyay. Decentralized Indirect Methods for Learning Automata Games. IEEE Transactions on Systems, Man, and Cybernetics, pp. 1213 -- 1223, vol. 41, issue 5, October, 2011.
64. *O. Tilak, *A. Hoblitzell, S. Mukhopadhyay, *Q. You, S. Fang, Y. Xia, and J. Bidwell. Multi- Level Text Mining for Bone Biology. (Invited), Concurrency and Computation: Practice and Experience journal, pp. 2355-2364, vol. 23, issue 17, Publisher: John Wiley, December, 2011.
65. R. Raje, S. Mukhopadhyay, *S. Phatak, and R. Shastri. Software Service Selection by Multi- Level Matching and Reinforcement Learning. 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS 2010), (sponsored by ACM SIGSIM), December 1-3, Boston, 2010
66. *O. Tilak and S. Mukhopadhyay. Decentralized and Partially Decentralized Reinforcement Learning for Distributed Combinatorial Optimization Problems. 9th IEEE International Conference on Machine Learning and Applications (ICMLA), December 12-14, Washington D.C, 2010.
67. S. Mukhopadhyay, M. Palakal, and *K. Maddu. “Multi-way Association Extraction and Visualization from Biological Text Documents Using Hyper-graphs: Applications to Genetic Association Studies for Diseases”, pp. 145–154, Artificial Intelligence in Medicine Journal, vol. 49, 2010.
68. *A. Hoblitzell, S. Mukhopadhyay, *Q. You, S. Fang, Y. Xia, and J. Bidwell. Text Mining for Bone Biology. pp. 522-530, Proceedings of the ACM International Symposium on High Performance Distributed Computing (HPDC), Emerging Computational Methods for the Life Sciences Workshop, Chicago, June 21–25, 2010.
69. *O.Tilak, S. Mukhopadhyay, M. Tuceryan, and R. Raje. A Novel Reinforcement Learning Framework for Sensor Subset Selection. pp. 95-100, Proceedings of he 2010 IEEE International Conference on Networking, Sensing, and Control, Chicago, IL, 2010.
70. K. S. Narendra and S. Mukhopadhyay. To Communicate or Not to Communicate: A Decision-Theoretic Approach to Decentralized Adaptive Control, pp. 6369–6376, Proceedings of the 2010 American Control Conference, Baltimore, MD on June 30-July 02, 2010, Paper Publisher: IEEE
71. M. Babbar-Sebens and S. Mukhopadhyay. “Reinforcement Learning for Human-Machine Collaborative Optimization: Application in Ground Water Monitoring”, pp. 3563-3568, The proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, Texas, 2009.
72. B. Cheng, *H. Vaka, and S. Mukhopadhyay. “Gene-Gene Association Study Between Breast Cancer and Osteoporosis Using Transminer Text Mining System”, pp. 411–414,
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The proceedings of the 2009 IEEE International Conference on Bioinformatics & Biomedicine (BIBM), Washington D.C, 2009 (acceptance rate: 35%).
73. S. Mukhopadhyay, S. Peng, R. Raje, M. Palakal, and J. Mostafa. “Performance and Processing Time of Some Information Filtering Systems on a Benchmark Text Data Set”, short paper, pp. 185–188, The Proceedings of The 21st International Conference on Software Engineering and Knowledge Engineering, Boston, MA, 2009.
74. *H. Vaka and S. Mukhopadhyay. “Knowledge Extraction and Extrapolation Using Ancient and Modern Biomedical Literature”, pp. 996–1001, Proceedings of the Second Biocomputing Workshop at IEEE AINA Conference, Bradford, UK, 2009.
75. S. Mukhopadhyay, M. Palakal, and *K. Maddu. “Multi-way Association Extraction From Biological Text Documents Using Hyper-graphs”, pp. 257–262, Proceedings of the IEEE Bioinformatics and Biomedicine (BIBM) Conference, Philadelphia, 2008
76. S. Mukhopadhyay and *N. Jayadevaprakash. “Automated Metadata Prediction and Its Application to Biological Association Discovery”, pp. 708–713, Proceedings of the First Biocomputing Workshop at IEEE AINA Conference, Okinawa, Japan, 2008.
Funded Projects:
1. National Science Foundation (NSF): “CAREER:Self-adjusting Models as a New
Direction In Machine Learning,” (PI: Murat Dundar), USD 499,999, 2013-2018.
2. National Science Foundation (NSF): “CAREER: A novel framework for mining graph
patterns in large biological and social networks” (Principal Investigator), USD 527, 427,
2012 – 2017
3. US Department of the Army, "Health-Terrain: Visualizing Large Scale Health Data", (PI:
Shiaofen Fang, Co-PI: Mathew Palakal, Shaun Grannis, Yuni Xia), $565,430, 2013-
2014,
4. Electronics and Telecommunications Research( ETRI), South Korea, "Development of
Key Technologies for Big Data Analysis and Management Software Based on Next
Generation Memory", (Institute PI: John Lee, Co-PI: Yuni Xia), $117,000, 2012-2014.
5. National Institute of Health (NIH): “Automated Spectral Data Transformations and
Analysis Pipeline for High Throughput Flow Cytometry,” (PI: Bartek Rajwa, co-PIs: Murat
Dundar and Alex Pothen), $410,000, 2012-2014.
6. IBM Research Award, "Large Scale Sensor Stream Analysis and Mining for Geriatric
Care", $19,000, 2011
7. National Institute of Health (NIH): "Machine-Learning Approach to Label-free Detection
of new Bacterial Pathogens," (PI: Murat Dundar and Bartek Rajwa), $385,000, 2010-
2012.
8. National Institute of Health (NIH): “Digital image analysis for quantitative and qualitative
assessment of pig islets,” (PI: Mike Green, Senior Investigators: Murat Dundar and
Bartek Rajwa), $250,000, 2010-2012.
9. National Institute of Health (NIH): “A Distributed Clinical and Biodefense National
Network for Rapid Organism Identification,” (PI: Paul Robinson, co-I: Murat Dundar,
Bartek Rajwa, and others), $1,309,177, 2010-2011.
10. National Science Foundation, "DisProt Database: A Central Repository of Information on
Intrinsically Disordered Proteins, (PI: Keith Dunker, Co-PI: Yuni Xia, Vladimir Uversky),
$1,425,995, 2009-2012.
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11. IBM Research Award, "TrafficAnalyzer: A Real-time Traffic Stream Processing and
Analyzing System", $20,000.
12. State of Indiana, "Invention of a Consumer-Side Geriatric Health Care Knowledge
Management and Decision Support System", (Institute PI: Mathew Palakal, Co-PI:
Josette Jones, Yuni Xia), $1,900,000, 2008-2010.
13. National Science Foundation, "NSF-EHCS (EHS), SM: Development of SYMBIOTE; A
Reconfigurable Logic Assisted Data Stream Management System for Multimedia Sensor
Networks", (PI: John Lee, Co-PI: Yuni Xia), $210,234, 2008-2010.
14. NSF/ESE: Spatial Interactive Optimization for Restoration of Upland Storage in
Watersheds: Community Participation in the Design of Distributed Practices and
Alternatives, (PI: M. Babbar-Sebens, co-PI: S. Mukhopadhyay), $410,000, 2010-2014.
15. NOAA: An interactive and participatory web-based optimization tool for supporting
community learning and collaborative design of adaptation action plans in watersheds,
(PI: M. Babbar-Sebens, co-PI: S. Mukhopadhyay), $100,000, 2014-2015.
16. NSF/ECS: Fast Reinforcement Learning using Multiple Models and State
Decomposition, (PIs: S. Mukhopadhyay and K. S. Narendra (Yale University)),
$356,000, 2014-2017.
Software Engineering, Distributed and Parallel Computing (SEDPC) Group
Referred Journal Publications, Books, & Book Chapters
1. *Peiris, M., & Hill, J. H. (2013). Adapting system execution traces to support analysis of software system performance properties. Journal of Systems and Software, 86(11), 2849-2862.
2. *Manjula Peiris and James H. Hill (2013). Non-intrusive Adaptation of System Execution Traces for Performance Analysis of Software Systems. State-of-the-Art Concepts and Future Directions in Software Engineering. Ed. Imran Ghani, Universiti Teknologi Malaysia (UTM), Malaysia.
3. *Pati, T., & Hill, J. H. (2012). A survey report of enhancements to the visitor software
design pattern. Software: Practice and Experience. 4. Hill, J. H. and Schmidt, D. C. (2012). Using Test Clouds to Enable Early Integration
Testing of Distributed Real-time and Embedded System Applications. Software Testing in the Cloud: Perspectives on an Emerging Discipline. Ed. Dr. Scott Tilley, Florida Institute of Technology, Melbourne, FL.
5. Hill, J. H., Varshneya, P., & Schmidt, D. C. (2011). Evaluating Distributed Real-time and
Embedded System Test Correctness using System Execution Traces. Central European Journal of Computer Science, 1(2), 167-184, Springer.
6. Hill, J. H., Sutherland, H., Staudinger, P., Silveria, T., Schmidt, D. C., Slaby, J. M., &
Visnevski, N. (2011, April) OASIS: An Architecture for Dynamic Instrumentation of Enterprise Distributed Real-time and Embedded Systems. International Journal of Computer Systems Science and Engineering, Special Issue: Real-time Systems.
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7. Hill, J. H. (2010). Data Mining System Execution Traces to Validate Distributed System Quality-of-Service Properties. Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains. Ed. Dr. A.V.Senthil Kumar, Bharathiar University, India.
8. Hill, J. H., Schmidt. D.C, Edmondson, J., and Gokhale, A. (2010, July/August). Tools for
Continuously Evaluating Distributed-System Qualities, IEEE Software, 27(4), 65-71. 9. White, J., Hill, J. H., Tambe, S., Gray, J., Gokhale, A., and Schmidt. D.C. (2009,
July/August). Improving Domain-specific Language Reuse through Software Product-line Configuration Techniques. IEEE Software Special Issue: Domain-Specific Languages and Modeling.
10. Yuanshun Dai, Yi Pan, Rajeev R. Raje (Eds.), Advanced Parallel and Distributed Computing: Evaluation, Improvement And Practice, Nova Science Publishers, Inc., ISBN: 1-60021-202-6, 2007.
11. Rajeev R. Raje, *Jayasree Gandhamaneni, Andrew M. Olson, Barrett R. Bryant, “MURDS:
A Mobile-Agent-based Distributed Discovery System”, Encyclopedia of Mobile Computing and Commerce, Volume 1, pp. 436-441, 2007.
12. Mihran Tuceryan, Rajeev R. Raje, “Distributed Heterogeneous Tracking for Augmented Reality Using Component-based Software Technologies”, Encyclopedia of Mobile Computing and Commerce, Volume 2, pp. 207-212, 2007.
13. Rajeev R. Raje, *Sivakumar Chinnasamy, Andrew Olson, *William Higdon, “The
Application and Enhancement of LePUS for Specifying Design Patterns”, Design Patterns FormalizationTechniques, pp. 236-257, 2007.
14. Rajeev R. Raje, *Alex Crespi, *Omkar J.Tilak, Andrew Olson, “An Access Control Model
for the Components in a Distributed System”, Handbook of Research on Information Assurance and Security, pp. 254-265, 2008.
15. *Omkar Tilak, Rajeev R. Raje, Andrew Olson, “Assurance for Temporal Compatibility
Using Contracts”, Handbook of Research on Information Assurance and Security, pp. 360-371, 2008.
16. *Changlin Sun, Rajeev R. Raje, Barrett Bryant, *Omkar Tilak, “Compositional Reasoning
of Performance in Component-Based Distributed Systems”, Cluster Computing, 11(4), pp. 331-340, 2008.
17. *Ketaki A. Pradhan, *Lahiru Gallege, Alfredo Moreno, Rajeev R. Raje, “MDE-URDS – A
Mobile Device Enabled Service Discovery System” (extended version of the conference paper), International Journal on Advanced Computing and Communication Networks, Vol. 3, No. 1 (2011), pp. 33-37, 2011.
18. *Anjali Kumari, *Ketaki A. Pradhan, *Lahiru S. Gallege, Rajeev R. Raje, “Synchronization
Level Specification and Matching of Software Components”, Software Engineering: An International Journal, Vol. 2, No. 1, pp. 7-19, March 2012.
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19. *Dimuthu U. Gamage, *Ryan Rybarczyk, Rajeev R. Raje, “A Practical Approach to Adaptive Service Composition”, Software Engineering: An International Journal, Vol. 3, No.1, pp. 7-20, April 2013.
20. Andrew Olson, Rajeev Raje, *Barun Devaraju, *Lahiru Gallege, “Learning Improves Service Discovery”, Concurrency and Computation: Practice and Experience, 2014 (To Appear).
Refereed Conference and Workshop Publications
1. Gunter Mussbacher, Daniel Amyot, Ruth Breu, Jean-Michel Bruel, Betty Cheng, Philippe Collet, Benoit Combemale, Robert France, Rogardt Heldal, James Hill, Jörg Kienzle, Matthias Schöttle, Friedrich Steimann, Dave Stikkolorum, Jon Whittle. (2014, September). Model-driven Engineering: Thirty Years From Now? ACM/IEEE 17th International Conference on Model Driven Engineering Languages and Systems (MODELS), Valencia, Spain
2. *Lakshmi Manohar Rao Velicheti, Dennis C. Feiock, *Manjula Peiris, Rajeev Raje, James H. Hill (2014, April). Towards Modeling the Behavior of Static Code Analysis Tools. 9th Cyber and Information Security Research Conference, Oak Ridge, TN.
3. *Manjula Peiris, James H. Hill, Jorgen Thelin, Sergey Bykov, Gabriel Kliot, and Christian Konig (2014, June). PAD: Performance Anomaly Detection in Multi-Server Distributed Systems. 7th IEEE International Conference on Cloud Computing, Alaska, USA.
4. Dennis Feiock and James H. Hill (2013, September). Using Component-based Middleware to Design and Implement Data Distribution Service (DDS) Systems. 39th Euromicro Conference on Software Engineering and Advanced Applications, Santander, Spain.
5. *Lahiru Gallege, *Dimuthu Gamage, James Hill and Rajeev Raje (2013, September). Trustworthy Service Selection using Long-term Monitoring of Trust Contracts. 17th IEEE International Enterprise Distributed Object Computing Conference (EDOC). Vancouver, BC.
6. *Manjula Peiris, Mohammad Al Hasan, and James H. Hill (2013, June). Auto-Constructing Dataflow Models from System Execution Traces. The 16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), Paderborn, Germany.
7. Dennis Feiock and James H. Hill (2013, June). Optimizing General-Purpose Software Instrumentation Middleware Performance for Distributed Real-time and Embedded Systems, The 16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), Paderborn, Germany.
8. *Darshan G. Puranik, Dennis C. Feiock, and James H. Hill (2013, April), Real-time Monitoring using AJAX and WebSockets. 20th Annual IEEE International Conference and Workshops on the Engineering of Computer Based Systems (ECBS), Tucson, AZ.
9. *Dimuthu U. Gamage, *Lahiru S. Gallege, James H. Hill, and Rajeev R. Raje (2012, December). A Compositional Trust Model for Predicting the Trust Value of Software System QoS Properties. 10th IEEE/IFIP International Conference on Embedded and Ubiquitous
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Computing: Middleware for Embedded and Ubiquitous Computing, October 3 – 5, 2012, Paphos, Cyprus.
10. Hill, J. H. (2012, June). Using Parameterized Attributes to Improve Testing Capabilities with Domain-specific Modeling Languages. 19th Annual IEEE International Conference and Workshops on the Engineering of Computer Based Systems (ECBS), Novi Sad, Serbia.
11. Hill, J. H. and Gokhale, A. (2012, June), Using Template Metaprogramming to Enhance
Reuse in Visitor-based Model Interpreters. 19th Annual IEEE International Conference and Workshops on the Engineering of Computer Based Systems (ECBS), Novi Sad, Serbia.
12. *Peiris, T. M and Hill, J. H. (2012, April). Adapting System Execution Traces for Validation of Distributed System QoS Properties. 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), Shenzhen, China.
13. Hill, J. and Schmidt, D.C. (2011, October). Experiences with Service-Oriented Middleware for Dynamic Instrumentation of Enterprise Distributed Real-time and Embedded Systems. 1st International Symposium on Secure Virtual Infrastructures (DOA-SVI'11), Crete, Greece.
14. *Gallege, L., *Gamage, D., Hill, J. H., and Raje, R. (2011, August). Towards a Comprehensive Approach for Integrating Trust into Enterprise DRE Systems. The 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011): Work In Progress Track. Toyoma, Japan.
15. *Pati, T and Hill, J. H. (2011, August). Real-time Monitoring of DRE Systems Using Dynamic Instrumentation Software. The 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011): Work In Progress Track. Toyoma, Japan.
16. *Peiris, T. M. and Hill, J. H. (2011, August). Towards Evolutionary Testing Component-based Distributed Real-time and Embedded (DRE) Systems in the Cloud. The 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011): Work In Progress Track. Toyoma, Japan.
17. Hill, J. H. (2011, August). Towards Heterogeneous Composition of Distributed Real-time and Embedded (DRE) Systems using the CORBA Component Model. 37th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA 2011), Oulu, Finland
18. Hill, J.H. (2011, April). Measuring and Reducing Modeling Effort in Domain-specific Modeling Languages with Examples. 18th IEEE International Conference and Workshops on Engineering of Computer-Based Systems, Las Vegas, NV.
19. *Owens, H. and Hill, J.H. (2011, March). Generating Valid Interface Definition Language from Succinct Models. The 14th IEEE International Symposium on Object/Component/Service-oriented Real-time Distributed Computing, Newport Beach, CA.
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20. Hill, J.H. (2011, March). Modeling Interface Definition Language Extensions (IDL3+) using Domain-Specific Modeling Languages. The 14th IEEE International Symposium on Object/Component/Service-oriented Real-time Distributed Computing, Newport Beach, CA.
21. *Peiris, T. M and Hill, J. H., (2010, August). Towards Adapting Non-Standard System Execution Traces for Validating Enterprise Distributed Real-time and Embedded System Quality-of-Service Properties. The 16th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA): Work In Progress Track. Macau SAR, P.R.C.
22. Hill, J. H. (2010, August). Context-based Analysis of System Execution Traces for Validating Distributed Real-time and Embedded System Quality-of-Service Properties. The 16th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). Macau SAR, P.R.C.
23. Hill, J. H., Sutherland, H., Staudinger, P., Silveria, T., Schmidt, D. C., Slaby, J. M., &
Visnevski, N. (2010, May). OASIS: A Service-Oriented Architecture for Dynamic Instrumentation of Enterprise Distributed Real-time and Embedded Systems. The 13th IEEE International Symposium on Object-/Component-/Service-Oriented Real-time Distributed Computing (ISORC), Carmona, Spain.
24. Hill, J. H. (2009, November). An Architecture Independent Approach to Emulating Computation Intensive Workload for Early Integration Testing of Enterprise DRE Systems. In Proceedings of the 11th International Symposium on Distributed Objects, Middleware, and Applications (DOA ‘09), Vilamoura, Algarve-Portugal.
25. *Manjula Peiris and James H. Hill (2013, November). Towards Detecting Software Performance Anti-patterns using Classification Techniques. 1st International Workshop on Machine Learning and Information Retrieval for Software Evolution, Automated Software Engineering (ASE), Palo Alto, California.
26. *Pati, T. and Hill, J. H. (2012, October). Proactive Modeling: Auto-Generating Models From Their Semantics and Constraints. The 12th Workshop on Domain-Specific Modeling, Tucson, AZ.
27. Hill, J. H. (2012, April). iCCM: A Framework for Servant-based Integration of DDS into the CORBA Component Model. OMG Workshop on Real-time, Embedded and Enterprise-Scale Time-Critical Systems (RTWS). Paris, France.
28. Hill, J. H., Feiock, D., and *Pati, T. (2012, April). OASIS: A for Real-time Instrumentation of Distributed Real-time and Embedded Systems. OMG Workshop on Real-time, Embedded and Enterprise-Scale Time-Critical Systems (RTWS). Paris, France.
29. *Peiris, T. M and Hill, J. H., (2011, August). Adapting System Execution Traces for Validation of Enterprise Distributed System QoS Properties. 5th International Workshop on Advances in Quality of Service Management (AQuSerM 2011), Helsinki, Finland.
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30. Hill, J. H. and *Owens, H. (2011, May). Towards Using Abstract Behavior Models to Evaluate Software System Performance Properties. 5th International Research Workshop on Advances and Innovations in Software Testing, Memphis, TN.
31. *Omkar Tilak, Rajeev R. Raje, “Temporal Interaction Contracts for Components in a Distributed System”, Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference EDOC 2007, Annapolis, Maryland, 2007.
32. *Girish Joshi, Rajeev Raje, Mihran Tuceryan, “Designing and Experimenting with a Distributed Tracking System”, Proceedings of the 14th IEEE International Conference on Parallel and Distributed Systems, Melbourne, Australia, 2008.
33. Rajeev R. Raje, Pratibha Katuri, *Anjali Kumari, *Omkar Tilak, “Multi-level Matching of Distributed Software Components”, Proceedings of the International Conference on Computer,Communication, and Instrumentation, Mumbai, India, 2009.
34. Snehasis Mukhopadhyay, Shengquan Peng, Rajeev R. Raje, Mathew Palakal, Javed Mostafa, “Performance and Processing Time of Some Information Filtering Systems on a Benchmark Text Data Set”, Proceedings of the 21st International Conference on Software Engineering and Knowledge Engineering, Boston, MA, 2009.
35. *Ketaki A. Pradhan, *Lahiru Gallege, Alfredo Moreno, Rajeev R. Raje, “MDE-URDS: A Mobile Device Enabled Service Discovery System”, Proceedings of the International Conference on Recent trends in Computing and Communications, Chennai, India, 2009.
36. *Omkar J. Tilak, Snehasis Mukhopadhyay, Rajeev R. Raje, Mihran Tuceryan, “A Novel Reinforcement Learning Framework for Sensor Subset Selection”, Proceedings of 2010 IEEE International Conference on Networking, Sensing, and Control, Chicago, IL, 2010.
37. Rajeev R. Raje, Snehasis Mukhopadhyay, *Sucheta Phatak, *Rashmi Shastri, *Lahiru Gallege, “Software Service Selection by Multi-Level Matching and Reinforcement Learning”, Proceedings of the 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems (in Cooperation with ACM SIGSIM), Boston, MA, 2010.
38. *Ryan Rybarczyk, Rajeev R. Raje, Mihran Tuceryan, “A Next-generation of a Distributed Tracking System”, Proceedings of the International Conference On Demand Computing, Banguluru, India, 2010.
39. *Lahiru Gallege, *Ketaki Pradhan, Rajeev R. Raje, “Experiments with a Multi-level Discovery System”, Proceedings of the 1st International Conference on Computing, New Delhi, India, 2010.
40. *Ryan Rybarczyk, Rajeev R. Raje, Mihran Tuceryan, “Enhancing a Distributed Tracking System”, Proceedings of the 3rd International Joint Conference on Information and Communication Technology, Mumbai, India, 2011.
41. *Lahiru S. Gallege, *Dimuthu U. Gamage, James H. Hill, Rajeev R. Raje, “A Case Study in Composing a Trusted Distributed Real-time and Embedded (DRE) System”, Proceedings of the International Conference On Network Infrastructure Management Systems, Mumbai, India, 2011.
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42. *Dimuthu U. Gamage, Zhisheng Huang, Andrew Olson, Rajeev R. Raje, “Creating QoS aware Distributed Computing Systems Using UniFrame Approach”, Proceedings of the 2nd International Conference on Computing, New Delhi, India, 2011.
43. *Lahiru S. Gallege, *Aboli J. Phadke, Rajeev R. Raje, Meghna Babbar-Sebens, “Cloud Service Selection from Earth Science Domain”, Proceedings of the 2nd International Conference on Recent Trends in Information Technology and Computer Science, Mumbai, India, 2012.
44. *Lahiru S. Gallege, *Dimuthu U. Gamage, James H. Hill, Rajeev R. Raje, “Trust Contract of a Service and its role in Service Selection for Distributed Software Systems”, Proceedings of the 8th Cyber Security and Information Intelligence Research Workshop, Oak Ridge, TN, 2013.
45. *Lahiru S. Gallege, *Dimuthu U. Gamage, James H. Hill, Rajeev R. Raje, “Experimental Evaluation of Trustworthiness of Compositional Systems”, Proceedings of the 2nd International Conference On Network Infrastructure Management Systems, Mumbai, India, 2013.
46. *Ryan Rybarczyk, Rajeev R. Raje, Mihran Tuceryan, “eDOTS 2.0: A Pervasive Indoor Tracking System”, Proceedings of the International Conference on Software Engineering and Knowledge Engineering (SEKE'13), Boston, MA, 2013.
47. *Lahiru S. Gallege, *Dimuthu Gamage, James H. Hill, Rajeev R. Raje, “Towards Trust-Based Recommender Systems for Online Software Services”, Proceedings of the 9th Cyber Security and Information Intelligence Research Workshop, Oak Ridge, TN, 2014.
48. *Aboli Phadke, *Ryan Rybarczyk, Rajeev R. Raje, Mihran Tuceryan, “Incorporating Mobile Devices in Indoor Tracking”, 3rd International Conference On Network Infrastructure Management Systems, Mumbai, India, 2014.
49. Song, F., Dongarra, J., “Scaling Up Matrix Computations on Shared-Memory Manycore Systems with 1000 CPU Cores”, 28th ACM International Conference on Supercomputing (ICS 2014), Munich, Germany, June 2014.
50. Waddington, D., Colmenares, J., Kuang, J., Song, F., “KV-Cache: A Scalable High-Performance Web-Object Cache for Manycore”, ACM/IEEE International Conference on Utility and Cloud Computing (UCC 2013), Dresden, Germany, December 2013.
51. Cao, C., Song, F., Waddington, D., “Implementing a High-Performance Recommendation System Using Phoenix++”, IEEE ICITST 2013, London, UK, December 2013.
Editorials
1. Hill, J. H. (2012, February). “I Want To Be A…”. Indianapolis Recorder. Indianapolis, IN.
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Refereed Tool Demonstrations
1. Hill, J. H. (2010, May). CUTS: A System Execution Modeling Tool for Realizing Continuous System Integration Testing. Proceedings of the ACM/IEEE 32nd International Conference on Software Engineering: Research Demonstrations Track. Cape Town, South Africa.
Abstracts and Posters
1. *Tejal B. Parulekar, Dennis C. Feiock, and James H. Hill (April, 2013). CORBA-JS: An Open-Standards Framework for Distributed Object Computing over the Web. IUPUI Research Day, Indianapolis, IN.
2. *Lakshmi Manohar Rao Velicheti, Dennis Feiock, Rajeev R. Raje, and James H. Hill (April, 2013). Qualitative and Quantitative Evaluation of Static Code Analysis Tools. IUPUI Research Day, Indianapolis, IN.
3. *Tanumoy Pati and James H. Hill (April, 2012). Auto-generating Models from Their Semantics and Constraints. IUPUI Research Day.
4. *Darshan Puranik and James H. Hill (April, 2012). Real-time Monitoring and Analysis of Distributed Software Systems via the Web. IUPUI Research Day.
5. *Manjula Peiris, Mohammad Al Hasan, and James H. Hill (April, 2012). Using System Execution Traces to Analyze Performance Properties of Software Systems. IUPUI Research Day.
6. *Lahiru S. Gallege, *Dimuthu U. Gamage, James H. Hill, and Rajeev R. Raje (April, 2012). Trusted Service Representation and Selection for Generating Distributed Real-time and Embedded (DRE) Systems. IUPUI Research Day.
7. *Dimuthu U. Gamage, *Lahiru S. Gallege, James H. Hill, and Rajeev R. Raje (April, 2012). Trusted Service Composition for Distributed Real-Time and Embedded (DRE) Systems. IUPUI Research Day.
8. Hill, J. H. and Gokhale, A. (2007, October). Validation of functional (in)correctness for large-scale component-based software systems using model-driven engineering. Poster session presented at the ACM/IEEE 10th International Conference on Model Driven Engineering Languages and Systems (MoDELS 07), Nashville, TN.
Internal and External Funding
Title: System Execution Modeling Environment Research and Development: Phase 1 – 5 (James H. Hill) Sponsor: Australia Defense Science and Technology Organization (DSTO) Period of Performance: 8/1/2009 - 12/31/2016 Funding: $464,211 USD Title: A Pervasive Computing Infrastructure for Supporting CS Graduate Courses (Raje, Tuceryan, Song, Liang) Sponsor: IUPUI School of Science teaching support
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Period of Performance: 2014-2015 Funding: $27,657 USD Title: Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phase 3 (Hill and Raje) Sponsor: Northrup Grumman via S2ERC Period of Performance: 2/1/2014 - 1/31/2015 Funding: $10,000 USD
Title: Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phase 2 (Hill and Raje) Sponsor: Department of Homeland Security via S2ERC Period of Performance: 8/1/2013 – 12/31/2014 Funding: $30,000 USD Title: Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phase 1 (Hill and Raje) Sponsor: Lockheed Martin & Northrup Grumman via S2ERC Period of Performance: 1/1/2013 – 12/31/2013 Funding: $49,060 USD
Title: An Integrated Architecture-Aware Framework Supporting Highly Scalable Scientific Computing for Many Cores (Song) Sponsor: IUPUI Period of Performance: 1/1/2013 – 12/31/2013 Funding: $8,000 USD
Title: Automatic Identification of Software Performance Anti-patterns in Cloud Computing Applications (Hill) Sponsor: Amazon Inc. Period of Performance: 1/1/2012 – 12/31/2013 Funding: $5,000 (no indirect cost allowed)
Title: Modeling, Specifying, Discovering, and Integrating Trust into Distributed Real-time and Embedded (DRE) Systems – Phases 1 and 2 (Rajeev Raje and James Hill) Sponsor: Air Force Research Lab vs S2ERC Period of Performance: 7/1/2011 – 1/31/2013 Funding: $79,000 USD
Title: Continued Support for Research and Development on System Integration Testing as a Service (Hill) Sponsor: Air Force Research Lab (AFRL) Period of Performance: 9/1/2010 – 12/31/2010 Funding: $10,000 USD Title: Cyber-physical multi-core Optimization for Resource & cachE effectS (C2ORES) (Hill) Sponsor: Office of Naval Research Primary Organization: Vanderbilt University Period of Performance: 7/3/2012 – 7/2/2013 Portion of Funding: $300,000 (IUPUI Portion $85,478 USD)
Title: EISA/OASIS Transition Project – Transition Planning, Phase 3 & Phase 4 (Hill) Sponsor: Science Applications International Corporation Primary Organization: Vanderbilt University
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Period of Performance: 11/1/10 - 5/15/2011 Portion of Funding: $348,350 (IUPUI Portion $88,269 USD)
Title: CoSMIC Extensions for the Scalable Node Architecture (Hill) Sponsor: Northrop Grumman Primary Organization: Vanderbilt University Period of Performance: 5/1/10 – 8/31/10 Portion of Funding: IUPUI Portion $47,599 USD
Title: Reducing Accidental Complexities Associated with CoSMIC Tool Suite – Phase 1 & 2 (Hill) Sponsor: Northrop Grumman Primary Organization: Vanderbilt University Period of Performance: 8/1/2009 – 8/31/2010 Portion of Funding: (IUPUI Portion $145,230 USD) Title: A Distributed Framework for Indoor Location Tracking (Raje, Tuceryan) Sponsor: Purdue Research Foundation Period of Performance: 7/1/2013 – 6/30/2014 Funding: $17,608 USD Title: Developing a Fast and Accurate Parallel Solver for Multi-scale Biochemical Reacting Systems (Chin, Raje) Sponsor: IUPUI MURI Period of Performance: 2009-2010 Funding: $2,000 USD Title: Developing Fast and Accurate Parallel Solver for Multi-Scale Chemically Reacting Systems (Chin, Raje) Sponsor: IUPUI MURI Period of Performance: 2008-2009 Funding: $1,500
Imaging and Visualization Group
Journal papers and book chapters
1. *Wan J, Zhang Z, Rao BD, Fang S, Yan J, Saykin AJ, Shen L, for the ADNI (2014) Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning. IEEE Trans. on Medical Imaging, 33(7):1476-1488.
2. *Qian You, Shiaofen Fang and Patricia Ebright, Iterative Visual Clustering for Learning Concepts from Unstructured Text Unstructured Text Data. International Journal of Software and Informatics, Volume 6, Issue 1 (2012), pp. 43-59.
3. *Jason Mclaughlin, Shiaofen Fang, Sandra Jacobson, H Eugene Hoyme, Luther Robinson and Tatiana Foroud. Interactive Feature Visualization and Detection for 3D Face Classification, International Journal of Cognitive Informatics and Natural Intelligence. 5(2), 2011, 1-16.
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4. *Andrew Hoblitzell, *Omkar Tilak, Snehasis Mukhopadhyay, *Qian You, Shiaofen Fang, Yuni Xia, Joseph Bidwell, Multi-Level text Mining for Bone Biology, Concurrency and Computation: Practice and Experience. 23(17), 2011, 2355-2364.
5. Xiaohong Mao, Jinghua Fu, Wei Chen, *Qian You, Shiaofen Fang, Qunsheng Peng, Structural Visualization of Sequential DNA Data,Journal of Zhejiang University-Science C (Computer & Electron), 12(4): 263-272, 2011.
6. Luoding Zhu, Guowei He, Shizhao Wang, Laura Miller, Xing Zhang, Ray Chin, *Qian You, Shiaofen Fang, An immersed boundary method by the lattice Boltzmann approach in three dimensions, Computers & Mathematics with Applications (CMA, by Elsevier), 61(12), pp 3506–3518.
7. *Qian You, Shiaofen Fang, Jake Chen, “GeneTerrain: Visual Exploration of Differential Gene Expression Profiles Organized in Native Biomolecular Interaction Networks”, Journal of Information Visualization, 2010; 9:1, 1-12.
8. Jianbing Shen, Hanqiu Sun, Jiaya Jia, Hanli Zhao, Xiaogang Jin, Shiaofen fang. A unified framework for designing textures using energy optimization, Pattern Recognition, 43:2, pp. 457-469, 2010.
9. Shengjun Xue, Geng Liu, Shiaofen Fang: The Research of Optimization of Browse Efficiency Based on Web Information on Small-Screen. FSKD (3) 2009: 495-499
10. Jianbing Shen, Shiaofen Fang, Hanli Zhao, Xiaogang Jin. Fast Approximation of Trilateral Filter for Tone Mapping Using a Signal Processing Approach. International Journal of Signal Processing, Elsevier, 2009, 89:5. 901-907.
11. Shiaofen Fang, *Basil George and Mathew Palakal. Automatic Surface Scanning of 3D Artifacts, International Journal of Virtual Reality. 2009, 8:4, 67-72.
12. Shiaofen Fang, *Jason McLaughlin, *Jiandong Fang, Jeffrey Huang, Ilona Autti-Rämö, Åse Fagerlund, et al., “Automated Diagnosis of Fetal Alcohol Syndrome Using 3D Facial Image Analysis”, Orthodontics and Craniofacial Research, 2008;11:162-171.
13. Jie Chen, Shuning Li and Shiaofen fang, “Quantification of Tooth Displacement from Cone Beam CT Images”, American Journal of Orthodontics & Dentofacial Orthopedics, 136:3, pp:393-400,2009.
14. B. Tan, A.D. Graham, G. Tsechpenakis, and M.C. Lin, “A Novel Analytical Method Using OCT to Describe the Corneoscleral Junction,” Optometry and Vision Science, 91(6):650-657, 2014.
15. X. Chang, M.D. Kim, R. Stephens, T. Qu, A. Chiba, and G. Tsechpenakis, “Part-based Motor Neuron Recognition in the Drosophila Ventral Nerve Cord,” Elsevier NeuroImage, 90:33-42, 2014.
16. G. Tsechpenakis, *P. Mukherjee, M. D. Kim, and A. Chiba, “Three-dimensional Motor Neuron Morphology Estimation in the Drosophila Ventral Nerve Cord,” IEEE Trans on Biomedical Engineering, 59(5):1253-1263, 2012
17. S.P. Chatzis, G. Tsechpenakis, “A Possibilistic Clustering Approach Towards Generative Mixture Models,” Pattern Recognition,45(5):1819-1825, 2012.
18. R.B. Montero, X. Vial, D.T. Nguyen, *S. Farhand, M. Reardon, S.M. Pham, G. Tsechpenakis, F.M. Andreopoulos, “Electrospun gelatin scaffolds with controlled nano-architecture and biological cues for directed angiogenesis,” Elsevier Acta Biomaterialia, 8(5):1778-1791, 2012.
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19. H. Layman, X. Li, E. Nagar, A. Azar, S.M. Pham, G. Tsechpenakis, and F.M. Andreopoulos, "Synergistic angiogenic effect of co-delivering FGF-2 and G-CSF from fibrin scaffolds and bone marrow transplantation in critical limb ischemia," Tissue Engineering, Part A, 17(1-2), pp. 243-254, 2011.
20. G. Tsechpenakis and S. Chatzis, “Deformable Probability Maps: Probabilistic Shape and Appearance-based Object Segmentation,” Computer Vision and Image Understanding, 115(8), pp. 1157-1169, 2011.
21. S. Chatzis and G. Tsechpenakis, “The Infinite Hidden Markov Random Field Model,” IEEE Trans. on Neural Networks, 21(6), pp. 1004-1014, 2010.
22. Chouvatut, V.; Madarasmi, S. & Tuceryan, M. “3D face and motion estimation from sparse points using adaptive bracketed minimization,” Multimedia Tools and Applications, Springer US, vol. 63, pp. 569-589, 2013.
23. Chouvatut, V.; Madarasmi, S. & Tuceryan, M. “3D Face and Motion from Feature Points Using Adaptive Constrained Minima,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E94.A, pp. 2207-2219, 2011.
24. *Amirali Jazayeri; *Hongyuan Cai; Jiang Yu Zheng, Mihran Tuceryan “Vehicle Detection and Tracking in Car Video Based on Motion Model” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 583-595, June, 2011.
25. Mihran Tuceryan, Fang Li, Herbert L. Blitzer, Edwin T. Parks, Jeffrey A. Platt, “A Framework for Estimating Probability of a Match in Forensic Bite Mark Identification,” Journal of Forensic Sciences, vol. 56, no. S1, pp. S83-S89, 2011.
26. Wang, S.; Luo, S.; Huang, Y.; Zheng, J.; Dai, P. & Han, Q. “Railroad online: acquiring and visualizing route panoramas of rail scenes,” The Visual Computer, Springer Berlin Heidelberg, 2013, pp. 1-13.
27. *Cai, H. & Zheng, J. Y. “Key views for visualizing large spaces,” Journal of Visual Communication and Image Representation , 2009, vol. 20, pp. 420 – 427.
28. Zheng, J. & Wang, X. “Streaming Route Panorama for Seamless City Guidance,” International Journal of Computers and Applications, Anaheim, CA: ACTA Press, 2008, vol. 30, pp. 192-200.
29. Zheng, J. & *Shi, M. “Scanning Depth of Route Panorama Based on Stationary Blur,” International Journal of Computer Vision, Springer US, 2008, vol. 78, pp. 169-186.
30. S. Luo, J. Y. Zheng, State-of-art of video based smoke detection algorithms, Journal of Image and Graphics, 2013, Vol. 18, no. 1, 1225-1236.
31. B. Zhang, Y. Kado, K. Hattori, J.Y. Zheng, “Digital Scope on 2D Communication Sheet for Location-Specific Multimedia Service.” INTERACTIVE MULTIMEDIA, pp. 177-190, 2012.
32. Zheng, Jiang Yu. “Using Line Cameras for Monitoring and Surveillance Sensor Networks.” Image Processing: Concepts, Methodologies, Tools, and Applications. IGI Global, pp. 1032-1050. 2013
33. Shen L, Kim S, *Wan J, West JD, Saykin AJ. Fourier methods for 3D surface modeling and analysis. Emerging Topics in Computer Vision and Its Applications, World Scientific Pub Co Inc, 2011. (Book Chapter)
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Publications in blind peer-reviewed conference proceedings
1. Shiaofen Fang, Lanfang Miao and *Eric Lin. Visualization and Clustering of Online Book Reviews. To Appear in Proc. of 2014 International Conference on Information Visualization, Theory and Applications, Lisbon, Portugal, January, 2014.
2. Y. Xia, S. Fang, M. Palakal, R. Gamache, *T. Nguyen, *S. Bloomquist, *J. Keiper, S. Grannis. Data Exploration of a Notifiable Condition Detector System. In Proc. 2013 Workshop on Visual Analytics in Healthcare. Poster Presentation, Washington DC, Oct. 2013, pp 66-67.
3. M. Palakal, S. Fang, Y. Xia, S. Grannis, R. Gamache, *T. Nguyen, *S. Bloomquist, *J. Keiper. Detecting Comorbidity of Chlamydia from Clinical Reports. In Proc. 2013 Workshop on Visual Analytics in Healthcare. Poster Presentation, Washington DC. Oct. 2013, pp 75-76.
4. *Eric Lin, Shiaofen Fang, Jie Wang. Mining Online Book Reviews for Sentimental Clustering. 27th International Conference on Advanced Information Networking and Applications Workshops, 2013, pp.179-184.
5. Yishi Guo, Yang Wang, Shiaofen Fang, Hongyang Chao, Andrew J. Saykin1, Li Shen. Pattern Visualization of Human Connectome Data. Eurographics Conference on Visualization (EuroVis) (2012) M. Meyer and T. Weinkauf (Editors). Pp 78-83.
6. *Jing Wan, Zhilin Zhang, Jingwen Yan, Taiyong Li, Bhaskar Rao, Shiaofen Fang, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen. Sparse Bayesian Multi-Task Learning for Predicting Cognitive Outcomes from Neuroimaging Measures in Alzheimer's Disease. CVPR2012: IEEE International Conference on Computer Vision and Pattern Recognition, June 18-20, 2012.
7. Li T, *Wan J, Zhang Z, Yan J, Kim S, Risacher SL, Fang S, Beg MF, Wang L, Saykin AJ, Shen L, for the ADNI (2012) Hippocampus as a predictor of cognitive performance: Comparative evaluation of analytical methods and morphometric measures. MICCAI 2012 Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders, Nice, France, October 5, 2012
8. *Jason Mclaughlin, *Qian You, Shiaofen Fang, Jake Chen. TAO: Terrain Analytic Operators for Expert-Guided Data Mining Applications, 2011 Workshop on Visual Analytics in Healthcare (VisWeek 2011), pp.56-59, 2011.
9. *Jing Wan, Sungeun Kim, Mark Inlow, Kwangsik Nho, Shanker Swaminathan, Shannon L. Risacher, Shiaofen Fang, Michael W. Weiner, M. Faisal Beg, Lei Wang, Andrew J. Saykin, Li Shen, Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models. MICCAI 2011, Lecture Notes in Computer Science (LNCS) 6892:376-383, Springer, Heidelberg, 2011.
10. Shiaofen Fang, *Ying Liu, Sophia Vinci-Booher, Bruce Anthony and Feng Zhou, Surface Analysis from Video Volumes for Fetal Alcohol Syndrome Classification, Proc. International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2010, Sydney, pp. 22-26.
11. *Wan J, Shen L, Fang S, *McLaughlin J, Autti-Ramo I, Fagerlund A, Riley E, Hoyme HE, Moore ES, Foroud T, CIFASD. A framework for 3D analysis of facial morphology in fetal alcohol syndrome. H. Liao et al. (Eds.): MIAR 2010, Lecture Notes in Computer Science (LNCS) 6326, pp. 118-127, 2010.
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12. *Jason Mclaughlin, Shiaofen Fang, Jeffrey Huang, Sandra Jacobson, H Eugene Hoyme, Luther Robinson and Tatiana Foroud. Interactive Feature Visualization and Detection for 3D Face Classification, Proc. The 9th IEEE International Conference on Cognitive Informatics. July, 2010.
13. *Qian You, Shiaofen Fang and Patricia Ebright, Iterative Visual Clustering for Unstructured Text Data. Proc. International Symposium on Biocomputing, Feb. 2010, Calicut, India.
14. *Qian You, Shiaofen Fang, Luoding Zhu, "Visualizing Vortex Shedding of an Elastic Plate Interacting with a 3D Viscous Flow," vol. 2, pp.312-317, 2009 Ninth IEEE International Conference on Computer and Information Technology, 2009.
15. *Qian You, Shiaofen Fang, Snehasis Mukhopadhyay, Harsha Vaka, Jake Chen. Visualizing a Multi-level Graph of Biology Entity Interactions, 12th International Conference on Network Based Information Systems (NBIS’09), pp.304-309, 2009.
16. *Jing Wan, Li Shen, Kelsey E. Sheehan, Sungeun Kim, Robert M. Roth, James Ford, Shiaofen Fang, Andrew J. Saykin, and Heather A. Wishart, Shape Analysis of Thalamic Atrophy in Multiple Sclerosis, In Proc. MICCAI workshop on Medical Image Analysis on Multiple Sclerosis (validation and methodological issues). pages 93-104, September 20, 2009.
17. Shiaofen Fang, *Ying Liu, Jeffrey Huang, Sophia Vinci-Booher, Bruce Anthony, and Feng Zhou. “Alchohol Exposure Analysis of Mouse Embryos Using Multi-angle Facial Image Analysis”. Proc. of ACM Symposium on Applied Computing, 2009, 852-856.
18. *Adebayo Olowoyeye, Mihran Tuceryan, and Shiaofen Fang. Medical Volume Segmentation Using Bank of Gabor Filters. Proc. of ACM Symposium on Applied Computing, 2009, 826-830.
19. *Kai Wang, *Yan Sui, Xukai Zou, Arjan Durresi, and Shiaofen Fang, “Pervasive and Trustworthy Healthcare”, Worshop Proceeding, 22nd IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA) -- Bio-Computing Workshop, 750-755, 2008.
20. *Duoduo Liao and Shiaofen Fang, “A Volumetric Framework for the Modeling and Rendering of Dynamic and Heterogeneous Scenes”, Lecture Note in Computer Science (LNCS) Volume 4975, 585-591. Also in Proc. 2008 International Conference in Geometric Modeling and Processing, 2008.
21. *Qian You, Shiaofen Fang, and Patricia Ebright, “Visualizing Unstructured Text Sequences Using Iterative Visual Clustering”, 9th International Conference on Visual Information Systems, Lecture Notes on Computer Science series, Vol. 4781/2007, June 2007, Revised Selected Papers, Springer-Verlag, 2007, pp. 275-284.
22. X. Chang, M.D. Kim, R. Stephens, T. Qu, A. Chiba, and G. Tsechpenakis, “Neuron Recognition with Hidden Neural Network Random Fields,” Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), Beijing, China, 2014. (Tsechpenakis and his mentees Xiao, Stephens, and Qu are the main contributors, >90% of the work.)
23. *P. Mukherjee, M.D. Kim, A. Chiba, and G. Tsechpenakis, “Active Geometric Model: Application to Neuron Morphology Estimation in the Drosophila Ventral Nerve Cord,” Int’l Conference on Image Processing, Melbourne, Australia, 2013. (Tsechpenakis and his student, Mukherjee, are the main contributors, >90% of the work.)
24. *S. Farhand, F.M. Andreopoulos, and G. Tsechpenakis, “CRF-driven Multi-compartment Geometric Model,” Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), San
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Francisco, USA, 2013. (Tsechpenakis and his student, Farhand, are the main contributors, >90% of the work.)
25. X. Chang, M.D. Kim, A. Chiba, and G. Tsechpenakis, "Motor Neuron Recognition in the Drosophila Ventral Nerve Cord," Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), San Francisco, USA, 2013. (Tsechpenakis and his postdoc, Chang, are the main contributors, >90% of the work.)
26. *S. Farhand, R.B. Montero, X. Vial, D.T. Nguyen, M. Reardon, S.M. Pham, F.M. Andreopoulos, and G. Tsechpenakis, "Probabilistic Multi-compartment Geometric Model: Application to Cell Segmentation," Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), Barcelona, Spain, 2012. (Tsechpenakis and his student, Farhand, are the main contributors, >90% of the work.)
27. X. Chang, M.D. Kim, A. Chiba, and G. Tsechpenakis, "Patterning Motor Neurons in the Drosophila Ventral Nerve Cord using Latent State Conditional Random Fields," Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), Barcelona, Spain, 2012. (Tsechpenakis and his postdoc, Chang, are the main contributors, >90% of the work.)
28. G. Tsechpenakis, *R. Egoda Gamage, M.D. Kim and A. Chiba, "Motor Neuron Morphology Estimation for its Classification in the Drosophila Brain," IEEE Engineering in Medicine and Biology Conference, Boston, MA, Sept. 2011. (Tsechpenakis and Egoda Gamage are the main contributors, >90% of the work.)
29. A.W. Irvine†, S. Chatzis, and G. Tsechpenakis, “Which Brainstem Cells Generate the Respiration Cycles?,” 7th Int’l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), Rotterdam, Netherlands, April 2010. (All authors contributed equally.)
30. *Ruwan Egoda Gamage; *Abhishek Joshi; Jiang Y. Zheng; Mihran Tuceryan, “A 3D Impression Acquisition System for Forensic Applications” in Advances in Depth Image Analysis and Applications, Lecture Notes on Computer Science, v. 7854, Jiang, X.; Bellon, O.; Goldgof, D. & Oishi, T. (Eds.), Springer Berlin Heidelberg, pp. 9-20, 2013.
31. *Rybarczyk, R, Raje, R, Tuceryan, M, “eDOTS 2.0: A Pervasive Indoor Tracking System,” In Proceedings of the International Conference on Software Engineering and Knowledge Engineering (SEKE'13), pp. 429-434, Boston, MA, 2013.
32. *Ruwan Egoda Gamage, *Abhishek Joshi, Jiang Yu Zheng, Mihran Tuceryan. “A High Resolution 3D Tire and Footprint Impression Acquisition for Forensics Applications,” Proc. of Workshop on Applications of Computer Vision (WACV), Clearwater, Florida, pp. 317-322, January, 2013.
33. *Ryan Rybarczyk, Rajeev Raje, Mihran Tuceryan, “Enhancing a Distributed Tracking System,” Proceedings of 3rd International Joint Conference on Information and Communication Technology (IJcICT- 2011), 7 pages, Mumbai, India, 2011.
34. *Ryan Rybarczyk, Rajeev Raje, Mihran Tuceryan, “e-DTS 2.0 - A next-generation of a Distributed Tracking System,” in Proceedings of the International Conference on “On Demand Computing” (ICODC-2010), pp. 140-149, Bangalore, India. Nov. 3-4, 2010.
35. *Amirali Jazayeri, *Hongyuan Cai, Mihran Tuceryan, Jiang Yu Zheng, “Smart Video Systems in Police Cars,” 2010 ACM Multimedia Conference (MM 2010), pp. 807-810, Firenze, Italy, October 2010.
36. *Amirali Jazayeri, *Hongyuan Cai, Jiang Yu Zheng, Mihran Tuceryan, “Identifying Vehicles in In-Car Video Based on Motion Model,” IEEE Intelligent Vehicles Symposium (IV 2010), pp. 493-499, San Diego, June 2010.
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37. *Omkar Tilak, Snehasis Mukhopadhyay, Mihran Tuceryan, and Rajeev Raje, “A Novel Reinforcement Learning Framework for Sensor Subset Selection,” in Proceedings of the 2010 IEEE International Conference on Networking, Sensing, and Control (ICNSC 2010), pp. 95-100, Chicago, IL, 2010.
38. Varin Chouvatut, Suthep Madarasmi, and Mihran Tuceryan, “3D Reconstruction and Camera Pose from Video Sequence Using Multi-dimensional Descent,” in 4th International Conference on Information Systems, Technology and Management, Bangkok, Thailand, March 10-12, 2010, ICISTM 2010, pp. 282-292, also in book series: Communications in Computer and Information Science (CCTS) v. 54, Springer Verlag, Berlin, Prasad, S. K.; Vin, H. M.; Sahni, S.; Jaiswal, M. P. & Thipakorn, B. (eds.)
39. Varin Chouvatut, Suthep Madarasmi, and Mihran Tuceryan, “Face Reconstruction and Camera Pose Using Multi-dimensional Descent,” in Proceedings of the International Conference on Computer, Electrical, and Systems Science, and Engineering (CESSE 2009), World Academy of Science, Engineering and Technology, Volume 60, December 2009, pp. 730-735, Bangkok, Thailand, December 25-27, 2009, ISSN: 2070-3724.
40. *Adebayo Olowoyeye, Mihran Tuceryan, Shiaofen Fang, “Medical Volume Segmentation using Bank of Gabor Filters,” in Proceedings of the 24th Annual ACM Symposium on Applied Computing (SAC), Honolulu, Hawaii, pp. 826-830, March 2009.
41. *Glenn Flora, Mihran Tuceryan, Herb Blitzer, “Forensic Bite Mark Identification Using Image Processing Methods,” in Proceedings of the 24th Annual ACM Symposium on Applied Computing (SAC), pp. 903-907, Honolulu, Hawaii, March 2009.
42. *Amirali Jazayeri, *Hongyuan Cai, Jiang Yu Zheng, Mihran Tuceryan, Herbert Blitzer, “An Intelligent Video System for Vehicle Localization and Tracking in Police Cars,” in Proceedings of the 24th Annual ACM Symposium on Applied Computing (SAC), pp. 939-940, Honolulu, Hawaii, March 2009.
43. D. S. Greer, M. Tuceryan (2008). “Image Retrieval and Classification Using Associative Reciprocal-image Attractors.” IEEE International Conference on Image Processing, pp. 713-716, San Diego, CA, Oct, 2008.
44. *Girish G. Joshi, Rajeev R. Raje, Mihran Tuceryan (2008), “Designing and Experimenting with a Distributed Tracking System,” in 14th Intl Conference on Parallel and Distributed Systems, pp. 64-71, Melbourne, Australia, Dec 2008.
45. *Alex Leykin and Mihran Tuceryan (2007). Detecting Shopper Groups in Video Sequences, in Proceedings of the IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007), pp. 417-422, London, UK, September 2007.
46. *Dihong Gong; Jiangyu Zheng, “A Maximum Correlation Feature Descriptor for Heterogeneous Face Recognition,” 2nd IAPR Asian Conference on Pattern Recognition (ACPR), pp.135-139, Nov 5-8, 2013.
47. Shengchun Wang; Siwei Luo; Yaping Huang; Jiang Yu Zheng; Peng Dai; Qiang Han, “Rendering Railway Scenes in Cyberspace Based on Route Panoramas,” 2013 International Conference on Cyberworlds (CW), pp.12-19, Oct 21-23, 2013.
48. Shengchun Wang; Jiang Yu Zheng; Siwei Luo; Xiaoyue Luo; Yaping Huang; Dalong Gao, “Route panorama acquisition and rendering for high-speed railway monitoring,” 2013 IEEE International Conference on Multimedia and Expo (ICME), pp.1-6, July 15-19, 2013.
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49. *Hongyuan Cai; Jiang Yu Zheng, “Automatic heterogeneous video summarization in temporal profile,” 21st International Conference on Pattern Recognition (ICPR, pp. 2796-2800, Nov 11-15, 2012.
50. *Kilicarslan, M.; Zheng, J. Y., “Towards collision alarming based on visual motion,” 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.654-659, Sept 16-19, 2012.
51. *Kilicarslan, M.; Zheng, J. Y., “Modeling potential dangers in car video for collision alarming,” IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp.195-200, July 24-27, 2012.
52. *Hongyuan Cai and Jiang Yu Zheng. “Video anatomy: cutting video volume for profile.” In Proceedings of the 19th ACM international conference on Multimedia (MM '11). ACM, New York, NY, USA, pp. 1065-1068, 2011.
53. Jiang Yu Zheng; *Hongyuan Cai; Prabhakar, Karthik, “Profiling video to visual track for preview,” IEEE International Conference on Multimedia and Expo (ICME), pp.1,6, July 11-15, 2011.
54. *Hongyuan Cai and Jiang Yu Zheng. “Digesting omni-video along routes for navigation.” In Proceedings of the international conference on Multimedia (MM '10). ACM, New York, NY, USA, pp. 647-650, 2010.
55. Youiti Kado, Bing Zhang, and Jiang Yu Zheng. “Media distribution over 2D communication sheet.” In Proceedings of the international conference on Multimedia (MM '10). ACM, New York, NY, USA, pp. 1539-1542, 2010.
56. *Hongyuan Cai; Jiang Yu Zheng; Tanaka, H., “Acquiring shaking-free route panorama by stationary blurring,” 17th IEEE International Conference on Image Processing (ICIP), pp.921-924, Sept 26-29, 2010.
57. Kado, Y.; Bing Zhang; Jiang Yu Zheng, “Digital scope on communication sheet for media interaction,” IEEE International Conference on Multimedia and Expo (ICME), pp.974-979, July 19-23, 2010.
58. Mangesh Chitnis, Yao Liang, Jiang Yu Zheng, Paolo Pagano, and Giuseppe Lipari, “Wireless line sensor network for distributed visual surveillance.” In Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks (PE-WASUN '09). ACM, New York, NY, USA, pp. 71-78, 2009.
59. Jiang Yu Zheng; Jiang Yu Zheng; *Hongyuan Cai, “Pervasive Scene Map on Wireless Devices for City Navigation,” International Conference on Network-Based Information Systems (NBIS '09), pp.75-82, Aug 19-21, 2009.
60. Jiang Yu Zheng. “Scene map on wireless mobile platform.” In Proceedings of the 2009 ACM symposium on Applied Computing (SAC '09). ACM, New York, NY, USA, pp. 217-218, 2009.
61. Jiang Yu Zheng; *Bhupalam, Y.; Tanaka, H.T., “Understanding vehicle motion via spatial integration of intensities,” 19th International Conference on Pattern Recognition (ICPR 2008), pp.1-5, Dec 8-11, 2008.
62. *Hongyuan Cai and Jiang Yu Zheng. “Locating key views for image indexing of spaces.” In Proceedings of the 1st ACM international conference on Multimedia information retrieval (MIR '08). ACM, New York, NY, USA, pp. 31-38, 2008.
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63. Jiang Yu Zheng and Shivank Sinha. “Line cameras for monitoring and surveillance sensor networks.” In Proceedings of the 15th international conference on Multimedia (MULTIMEDIA '07). ACM, New York, NY, USA, pp. 433-442, 2007.
64. *Glenn R. Flora and Jiang Y. Zheng. “Adjusting route panoramas with condensed image slices.” In Proceedings of the 15th international conference on Multimedia (MULTIMEDIA '07). ACM, New York, NY, USA, pp. 815-818, 2007.
65. Zheng, J. Y. & Wang, X., “View Planning for Cityscape Archiving and Visualization,” Computer Vision – Asian Conference on Computer Vision (ACCV 2007), Lecture Notes on Computer Science (LNCS), Yagi, Y.; Kang, S.; Kweon, I. & Zha, H. (Eds.) Springer Berlin Heidelberg, vol. 4843, pp. 303-313, 2007.
66. Zheng, J. & *Shi, M., “Depth from Stationary Blur with Adaptive Filtering” Computer Vision – Asian Conference on Computer Vision (ACCV 2007), Lecture Notes on Computer Science (LNCS), Yagi, Y.; Kang, S.; Kweon, I. & Zha, H. (Eds.) Springer Berlin Heidelberg, vol. 4844, pp. 42-52, 2007.
67. Shen L, Kim S, Qi Y, Inlow M, Swaminathan S, Nho K, *Wan J, Risacher S, Shaw L, Trojanowski J, Weiner M, Saykin A, ADNI. Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. MBIA 2011: Lecture Notes in Computer Science (LNCS) vol. 7012, pp. 27-34, Springer, Heidelberg, 2011.
68. *Wan J, Zhang Z, Yan J, Li T, Rao B, Fang S, Kim S, Risacher S, Saykin A, Shen L, for the ADNI (2012) Sparse Bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer’s disease. Proceedings of the IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR’12), pp. 940-947, Providence, Rhode Island, June 18-20, 2012.
69. Yan J, Risacher SL, Kim S, Simon JC, Li T, *Wan J, Wang H, Huang H, Saykin AJ, Shen L, for the ADNI (2012) Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. MBIA’12: MICCAI Workshop on Multimodal Brain Image Analysis, Nice, France, October 1, 2012. Multimodal Brain Image Analysis, Lecture Notes in Computer Science Volume 7509, 2012, pp. 1-17.
70. Li T, *Wan J, Zhang Z, Yan J, Kim S, Risacher SL, Fang S, Beg MF, Wang L, Saykin AJ, Shen L, for the ADNI (2012) Hippocampus as a predictor of cognitive performance: Comparative evaluation of analytical methods and morphometric measures. NIBioAD'12: MICCAI Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders, pp. 133-144, Nice, France, October 5, 2012.
71. Yan J, Li T, Wang H, Huang H, *Wan J, Nho K, Kim S, Risacher SL, Saykin AJ, Shen L, for the ADNI (2012) Identification of novel cortical surface biomarkers for predicting cognitive outcomes based on group-level L-21 norm. NIBioAD'12: MICCAI Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders, pp. 207-216, Nice, France, October 5, 2012.
ABSTRACTS
1. B.C. Samuels, *N.M. Hammes, L. Racette, and G. Tsechpenakis, "Analysis of Macular Retinal Thickness in Arcuate Bins for Glaucoma Detection," The Association for Research in Vision and Ophthalmology (ARVO), 2014.
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2. B. Tan, Y. Zhou, T. Yuen, A.D. Graham, G. Tsechpenakis, and M.C. Lin, "Fluorogram of post-lens tear mixing under soft contact lenses ," The Association for Research in Vision and Ophthalmology (ARVO), 2014.
3. X. Chang, A. Kale, L. Dodge, J. Brazill, M.D. Kim, and G. Tsechpenakis, "Computer-aided Estimation of the Motor Neuron Morphology Patterns," Drosophila Genetics, Chicago, IL, 2012. [Selected for platform presentation]
4. *P. Mukherjee, J. Brazill, M.D. Kim, and G. Tsechpenakis, "Description and Visualization of Motor Neuron Morphology in Three Dimensions," Drosophila Genetics, Chicago, IL, 2012.
5. B. Tan, A.D. Graham, Y. Zhou, A. Ghanekar, J. Niimi, W. Li, G. Tsechpenakis, and M.C. Lin, “A Novel Analytical Method to Quantitatively Describe the Corneoscleral Junction using Optical Coherence Tomography,” The Association for Research in Vision and Ophthalmology (ARVO), Ft. Lauderdale, FL, 2012.
6. J. Brazill, X. Chang, G. Tsechpenakis, and M.D. Kim, “Identification of genes that regulate patterning in motor neuron dendrites,” Neurobiology of Drosophila, Cold Spring Harbor Laboratory, NY, 2011.
7. G. Tsechpenakis, H. Samarajeewa, M. Kim, A. Chiba, "Model Neurons of isPIN," 52nd Annual Drosophila Research Conference, San Diego, CA, 2011.
8. J.D. Baker, J. Bixby, W. Buchser, M. Blulina, X. Cai, S. Deng, V. Gupta, N. Johnson, T. Kagesawa, D. Kamiyama, M. Kim, V. Lemmon, T. Li, M. Ogihara, H. Samarajeewa, N. Sharifai, R. Smith, G. Tsechpenakis, G. Zhai, A. Chiba, "The isPIN Project," 52nd Annual Drosophila Research Conference, San Diego, CA, 2011.
9. Mihran Tuceryan, Herb Blitzer, Fang Li, Edwin T. Parks, Jeffrey A. Platt, and *Glenn Flora, “Use of 3-D Imaging and Mathematics in Assigning the Probability of a Match Between a Dental Model and a Bite Mark,” in Proceedings of the American Academy of Forensic Sciences, Denver, Colorado, Feb 2009.
10. *Ruwan Egoda Gamage, *Abhishek Joshi, Jiang Yu Zheng, Mihran Tuceryan, “A 3D Imaging Device for Tire and Footwear Impressions,” in Proceedings of the 65th Meeting of the American Academy of Forensic Sciences (AAFS 2013), Washington, DC, Feb 18-23, 2013.
11. Shen L, Ai H, Liang Y, *Wan J, Anthony B, Zhou FC, and the CIFASD Consortium. Influence of fetal alcohol on nasal bone development in c57bl/6j mouse models. RSA’11: 34th Annual RSA (Research Society on Alcoholism) Scientific Meeting, June 25-29, 2011, Atlanta, Georgia.
12. *Wan J, Fang S, Vinci-Booher S, Rogers J, Wetherill L, Robinson L, Hoyme E, Molteno C, Foroud T, Jacobson J, Jacobson S, Shen L, and the CIFASD Consortium. Effects of fetal alcohol on 3D facial morphology using surface-based morphometry. RSA’11: 34th Annual RSA (Research Society on Alcoholism) Scientific Meeting, June 25-29, 2011, Atlanta, Georgia.
13. *Wan J, Kim S, Nho K, Risacher S, Swaminathan S, Bertram L, Jack C, Weiner M, Beg F, Wang L, Saykin AJ, Shen L, ADNI. Influence of candidate AlzGene SNPs on hippocampal shape: A study of the ADNI cohort. OHBM’11: 17th Annual Meeting of the Organization for Human Brain Mapping, June 26-30, 2011, Québec City, Canada.
14. *Wan J, Kim S, McCullough K, Kwangsik Nho, Swaminathan S, West JD, Fang S, McHugh T, Flashman LA, Wishart HA, Rabin LA, Rhodes CH, Guerin SJ, Moore JH, Santulli RB, Saykin AJ, Shen L. Association analysis of candidate SNPs on hippocampal volume and shape in mild cognitive impairment and older adults with cognitive
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complaints. International Conference on Alzheimer's Disease (ICAD), July 10-15, 2010, Honolulu Hawaii. Selected for a platform talk.
15. Sheehan K, Wishart H, Roth R, Shen L, *Wan J, MacDonald J, Ford J, Seville J, Oliver B, Kasper L. The relationship of thalamic volume and shape to pain in relapsing-remitting MS. The European Conference on Treatment and Research in MS, Dusseldorf, Germany, Sept 2009.
16. *Wan J, Zhang Z, Fang S, Risacher SL, Saykin AJ, Shen L, for the ADNI (2013) Sparse Bayesian learning for identifying the neuroanatomical basis of cognitive impairment in AD. AAIC'13: Alzheimer's Association Int. Conf. on Alzheimer's Disease, Boston, MA, July 13-18, 2013.
17. *Wan J, Ramanan V, Kim S, West JD, Boddu M, Risacher SL, Fang S, Saykin AJ, Shen L(2012) Classification of AD and MCI using hippocampal subfields: an ADNI study. OHBM’12: Organization for Human Brain Mapping Ann Meeting, June 10-14, 2012, Beijing, China.
18. KADO, Y.; ZHANG, B. & ZHENG, J. Y. “Location Specific Contents Distribution over 2D Communication Sheet,” The Institute of Electronics, Information and Communication Engineers, vol. 110, no. 198, pp. 49-52, 2010.
19. Kado, Y.; Zhang, B. & Zheng, J. Y. “Location specific contents distribution over 2D
communication sheet” (放送方式・コンシューマエレクトロニクス), 映像情報メディア
学会, ITE technical report, vol. 34, no. 35, pp.77-80, 2010.
Internal and External Funding
1. Health-Terrain: Visualizing Large Scale Health Data, PI, Department of the Army – USAMRAA, W81XWH-13-1-0020, $661,035, 3/1/2013 – 8/31/2014.
2. REU Site: Enhancing Undergraduate Experience in Mobile Computing Security, co-PI (PI: Feng Li), National Science Foundation, NSF#1262984, $359,964, 6/1/2013 – 5/31/2016.
3. 3D Facial Imaging on FASD , co-PI (PI: Tatiana Foroud), National Institutes of Health (NIH), 9U01AA014809-04, $1,500,000, 06/01/08 – 05/31/13.
4. Mouse Model Neuro-Facial Dysmorphology: Translational and Treatment Studies, co-PI (PI: Feng Zhou), National Institutes of Health (NIH), 1U01AA017123-01, $1,200,000, 06/01/08 – 05/31/13.
5. NSF/DBI [#1252597]: CAREER: Modeling the Structure and Dynamics of Neuronal Circuits in the Drosophila larvae using Image Analytics [2013--2018]
6. NSF/DBI [#1062405]: ABI Innovation: Modeling the Drosophila Brain with Single-neuron Resolution using Computer Vision Methods [2011--2014]
7. This work is sponsored by Indiana University Collaborative Research Grant initiative [2013-3014]
8. This work was sponsored by NIH [#R21EB012136-01]: ‘Construction and profiling of biodegradable cardiac patches for the co-delivery of bFGF and G-CSF growth factors’, awarded to F.M. Andreopoulos (University of Miami). Subcontract to IUPUI.
9. This work was sponsored by NIH RC2[#36632]: ‘In situ Protein-Protein Interaction Networks (isPIN) of Neurons [2009-2011]’, awarded to A. Chiba (University of Miami). Subcontract to IUPUI.
10. (with Dr. Jiang Yu Zheng as co-PI), National Institute of Justice (NIJ): Digitizing Device to Capture Track Impressions, $253,120, September 2010–August 2012.
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11. (with Jiang Zheng as co-PI), “Advanced In-Car Video System,” subcontract from Institute for Forensic Imaging (IFI), $96,743. Original grant from National Institute of Justice (NIJ) to IFI for $270,000, September 2008–August 2010.
12. National Institute of Information and Communication Technologies (NICT) in Japan: PI, Sensor Network over 2D Communication LAN Sheet, $50,000
13. TOYOTA: Co-PI, 2014-2016 Vehicle testing scenario generation $1,500,000
Networking and Security Group
Referred Publications
1) H. Li, A. R. Kankanala and X. Zou, “ A Taxonomy and Comparison of Remote Voting Schemes,” ICCCN 2014, August 4–7, Shanghai, China (accepted)
2) *M. Rangwala, Z. Liang, W. Peng, X. Zou and F. Li, “ A Mutual Agreement Signature Scheme for Secure Data Provenance,” ICCCN 2014, August 1–7, Shanghai, China (accepted).
3) *W. Peng, F. Li, C. T. Huang, X. Zou, “A Moving-target Defense Strategy for Cloud-based Services with Heterogeneous and Dynamic Attack Surfaces,” ICC’2014 (Accepted)
4) *W. Peng, F. Li, K. Hand and X. Zou, “Moving-target Defense for Cloud Infrastructure: Lessons from Botnets,” High performance semantic cloud auditing and applications, New York: Springer Verlag, Book chapter, (in Press).
5) *Harold Owens, Arjan Durresi, Reliable Video over Software-Defined Networking, Globecom 2014, December 8-12, Austin, TX (accepted)
6) *Yefeng Ruan, *Lina Alfantoukh, Anna Fang, Arjan Durresi, Exploring Trust Propagation Behaviors in Online Communities, NBiS 2014, September 10-12, Salerno, Italy, (accepted)
7) *Lina Alfantoukh, Arjan Durresi, Techniques for Collecting data in Social Networks, NBiS 2014, September 10-12, Salerno, Italy, (accepted)
8) *Harold Owens, Arjan Durresi, Explicit Routing in Software-Defined Networking (ERSDN): Addressing Controller Scalability, NBiS 2014, September 10-12, Salerno, Italy, (accepted)
9) X. Zou, H. Li, *Y. Sui, *W. Peng, and F. Li, “Assurable, Transparent, and Mutual Restraining E-voting Involving Multiple Conflicting Parties,” INFOCOM’2014, Toronto, Canada, April 28–May 2, 2014, pp. 136–144.
10) *Y. Sui, X. Zou, Y. Du, and F. Li, “ Design and Analysis of a highly user-friendly, secure, privacy- preserving, and revocable authentication method,” IEEE Transactions on Computers, 63(4), pp. 902-916, April 2014. doi:10.1109/TC.2013.25.
11) *W. Peng, F. Li, X. Zou, and J. Wu, “ Behavioral Malware Detection in Delay Tolerant Networks,” IEEE Transactions on Parallel and Distributed Systems, 25 (1), pp. 53–63, 2014.
12) *W. Peng, F. Li, X. Zou, and J. Wu, “ A Two-stage Deanonymization Attack Against Anonymized Social Networks,” IEEE Transactions on Computers, 63(2), pp. 290–303, 2014.
13) *M. Rangwala, P. Zhang, X. Zou and F. Li, “A Taxonomy of Privilege Escalation Attacks in Android Applications,” International Journal of Security and Networks, Vol. 9, No. 1, 2014, pp. 40–55.
14) *W. Peng, F. Li, X. Zou, J. Wu, “Offloading Topical Cellular Content through Opportunistic Links,” The Tenth IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2013), October 14-16, in HangZhou, 2013, ZheJiang Province, P.R.China, pp.402 - 410.
15) H. Li, * Y. Sui, * W. Peng, X. Zou, and F. Li, “ A Viewable E-voting Scheme for Environments with Conflict of Interest,” IEEE Conference on Communications and Network Security, Oct. 14–16, 2013 Washington, D.C., USA, pp.251 - 259
16) *W. Peng, F. Li, K. J. Han, X. Zou, and J.Wu, “ T-dominance: Prioritized Defense Deployment for BYOD Security,” IEEE Conference on Communications and Network Security, Oct. 14–16, 2013 Washington, D.C., USA, pp.37 - 45.
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17) Feng Li, *Wei Peng, Chin-Tser Huang, and Xukai Zou, “Smartphone Strategic Sampling in Defending Enterprise Network Security”, ICC 2013, Dresden, Germany, pp.2155 - 2159.
18) *Y. Sui, X. Zou and Y. Du, “Cancellable Biometrics,” (book chapter), pp. 233–252, in Biometrics: from Fiction to Practice, Pan Stanford Publishing Pte. Ltd.,2013.
19) *Ping Zhang, Arjan Durresi, Leonard Barolli: “Policy-based mobility in heterogeneous networks.” J. Ambient Intelligence and Humanized Computing 4(3): 331-338 (2013)
20) Shpetim Latifi, Arjan Durresi, Betim Cico: “Separating network control from routers with Software Defined Networking”. BCI, 2013: 59
21) *Pawat Chomphoosang, *Yefeng Ruan, Arjan Durresi, Mimoza Durresi, Leonard Barolli: “Trust Management of Health Care Information in Social Networks.” CISIS 2013: 228-235
22) *Ping Zhang, Arjan Durresi, Raj Jain: “Cloud aided Internet mobility”. ICC 2013: 3688-3693
23) *Harold Owens II, Arjan Durresi: “Video over Software-Defined Networking (VSDN).” NBiS 2013: 44-51
24) Tamara Luarasi, Mimoza Durresi, Arjan Durresi: “Healthcare Based on Cloud Computing”. NBiS 2013: 113-118
25) Mimoza Durresi, Tamara Luarasi, Indrit Baholli, Arjan Durresi: “Targeted Advertisement Using Smartphones and Cloud Computing.” NBiS 2013: 126-133
26) *Y. Sui, X. Zou, Y. Du and F. Li, “ Secure and Privacy-preserving Biometrics based Active Authen- tication,” The 2012 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2012), pp.1291 - 1296.
27) *Y. Sui, X. Zou, F. Li and E. Y. Du, “Active User Authentication for Mobile Devices,” The 7th International Conference on Wireless Algorithms, Systems, and Applications (WASA 2012), pp. 540 - 548.
28) *W. Peng, F. Li, X. Zou, and J. Wu, “Seed and Grow: An Attack Against Anonymized Social Networks,” IEEE International Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’12), pp. 587 - 595.
29) *W. Peng, F. Li, X. Zou, and J. Wu, “A Privacy-Preserving Social-Aware Incentive System for Word- of-Mouth Advertisement Dissemination on Smart Mobile Devices,” IEEE International Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’12), pp. 596 - 604.
30) *Ping Zhang, Arjan Durresi, *Yefeng Ruan, “Trust based Security Mechanisms for Social Networks,” Proceedings of The 7th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2012), Victoria, Canada, November 12-14, 2012
31) Tao Yang , Leonard Barolli, Fatos Xhafa, Arjan Durresi and Makoto Takizawa, “MAPWC- 5: Implementation of a Wireless Sensor Network with Sensor Mote in Reality Environment,” Proceedings of The 7th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA-2012), Victoria, Canada, November 12-14, 2012
32) *Pawat Chomphoosang, Arjan Durresi, Leonard Barolli, “Trust Management of Social Networks in Health Care,” in the Proceedings of The 15th International Conference on Network-Based Information Systems (NBiS-2012), Melbourne, Australia September 26 - 28, 2012, pp. 1-7
33) Qi Wang, Leonard Barolli, Elis Kulla, Arjan Durresi, Aleksander Biberaj and Makoto Takizawa, “A Fuzzy-based Simulation System for Controlling Sensor Speed in Wireless Sensor Networks,” in the Proceedings of The 15th International Conference on Network-Based Information Systems (NBiS-2012), Melbourne, Australia September 26 - 28, 2012, pp. 1-8
34) Amitabh Mishra, Raj Jain, Arjan Durresi, “Cloud Computing: Networking and Communications Challenges,” accepted in IEEE Communication Magazine, 2012
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35) Mimoza Durresi, Arjan Durresi, Leonard Barolli, “Secure Inter-vehicle Communication,” in Proceedings of Sixth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2012, Palermo, Italy, July 5-7, 2012, pp. 177 – 183
36) Mimoza Durresi, Iliana Shukallari, Arjan Durresi, “Ad Hoc Wireless Communications for Marketing,” in Proceedings of Sixth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2012, Palermo, Italy, July 5-7, 2012, pp. 298 - 305.
37) Tao Yang, Tetsuya Oda, Leonard Barolli, Arjan Durresi, Makoto Takizawa, “Performance Evaluation of WSNs for Different MAC Protocols Considering TwoRayGround Radio Model and AODV Routing Protocol,” in Proceedings of Sixth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2012, Palermo, Italy, July 5-7, 2012, pp. 320 -326.
38) *Ping Zhang, Arjan Durresi, “Trust Framework for Social Networks,” in Proceedings of IEEE International Conference on Communications ICC2012, Ottawa, Canada, June 10-15, 2012, pp. 1-6
39) Tao Yang, Tetsuya Oda, Leonard Barolli, Jiro Iwashige, Arjan Durresi, Fatos Xhafa, “Investigation of Packet Loss in Mobile WSNs for AODV Protocol and Different Radio Models,” in Proceedings of IEEE AINA 2012, pp. 709-715
40) Qi Wang, Hironori Ando, Elis Kulla, Leonard Barolli, Arjan Durresi, “A Fuzzy-Based Cluster-Head Selection System for WSNs Considering Different Parameters,” in Proceedings of IEEE AINA 2012, pp. 962-967
41) Tao Yang, Gjergji Mino, Leonard Barolli, Arjan Durresi, Fatos Xhafa, “A Simulation System for Multi-mobile Sinks in Wireless Sensor Networks Considering TwoRayGround and Shadowing Propagation Models,” in Proceedings of BWCCA 2011, pp. 83-90.
42) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Network Trust Management in Emergency Situations,” Journal of Computer and System Sciences, Vol. 77, No. 4, 2011, pp. 677-686.
43) Hironori Ando, Qi Wang, Leonard Barolli, Elis Kulla, Arjan Durresi, Fatos Xhafa, “A Fuzzy-Based Cluster-Head Selection System for WSNs Considering Sensor Node Movement,” in Proceedings of INCoS 2011, pp. 130-135.
44) Tao Yang, Leonard Barolli, Jiro Iwashige, Arjan Durresi, Fatos Xhafa, “Comparison Evaluation of Static and Mobile Sensor Nodes in Wireless Sensor Networks Considering Packet-Loss and Delay Metrics, ” in Proceedings of INCoS 2011, pp. 196-202.
45) Tao Yang, Gjergji Mino, Leonard Barolli, Makoto Ikeda, Fatos Xhafa, Arjan Durresi, “Performance of Wireless Sensor Networks for Different Mobile Event Path Scenarios,” International Journal of Distributed Systems and Technologies IJDS,T 2(3): 49-63, 2011
46) Vladi Kolici, Keita Matsuo, Leonard Barolli, Fatos Xhafa, Arjan Durresi, Rozeta Miho, “Application of a JXTA-overlay P2P system for end-device control and e-learning,” Multimedia Tools Appl. 53(2): 371-389, 2011
47) Raj Jain, Arjan Durresi, Subarthi Paul, “Future internet architectures: design and deployment perspectives,” IEEE Communication Magazine, Vol. 49, No. 7, 2011, pp. 24-25
48) *Ping Zhang, Arjan Durresi, Raj Jain, “Economically Viable Support for Internet Mobility,in Proceedings of the IEEE International Conference on Communications ICC2011, Kyoto, Japan, June 5-9, 2011., pp. 1-6.
49) *Ping Zhang, Arjan Durresi, Leonard Barolli, “Survey of Trust Management on Various Networks,” in Proceedings of Fifth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2011, Seoul, Korea, June 30th-July 2nd, 2011, pp. 219-226.
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50) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Adaptive Wireless Routing Protocols,” in Proceedings of 14-th International Conference on Network-Based Information Systems NBiS 2011, Tirana, Albania, September 7- 9, 2011, pp. 302-309.
51) Yohei Nosaka, Tao Yang, Gjergji Mino, Leonard Barolli, Fatos Xhafa, Arjan Durresi, “Comparison Evaluation of Single and Multi Mobile Events Wireless Sensor Networks Using AODV Protocol,” in Proceedings of Fifth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2011, Seoul, Korea, June 30th-July 2nd, 2011, pp. 168-175.
52) Tao Yang, Gjergji Mino, Leonard Barolli, Arjan Durresi, Fatos Xhafa, “Energy-saving in Wireless Sensor Networks Considering Mobile Sensor Nodes,” in Proceedings of Fifth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2011, Seoul, Korea, June 30th-July 2nd, 2011, pp. 249-256.
53) Taku Ikebata, Gjergji Mino, Leonard Barolli, Fatos Xhafa, Arjan Durresi, Akio Koyama, “Performance Evaluation of FACS-MP CAC System Priority Algorithm for Wireless Cellular Networks,” in Proceedings of Fifth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2011, Seoul, Korea, June 30th-July 2nd, 2011, pp. 295-301.
54) Hironori Ando, Elis Kulla, Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama, “A New Fuzzy-based Cluster-Head Selection System for WSNs,” in Proceedings of Fifth International Conference on Complex, Intelligent, and Software Intensive Systems - CISIS 2011, Seoul, Korea, June 30th-July 2nd, 2011, pp. 432-437.
55) Leonard Barolli, Tao Yang, Gjergji Mino, Arjan Durresi, Fatos Xhafa, Makoto Takizawa, “Performance Evaluation of Wireless Sensor Networks for Mobile Sensor Nodes Considering Goodput and Depletion Metrics,” in Proceedings of The 9th IEEE International Symposium on Parallel and Distributed Processing with Applications ISPA 2011, Busan, Korea, 26-28 May 2011, pp. 63-68
56) Tao Yang, Gjergji Mino, Leonard Barolli, Arjan Durresi, Fatos Xhafa, “Comparison Evaluation for Mobile and Static Sensor Nodes in Wireless Sensor Networks Considering Two Ray Ground and Shadowing Propagation Models,” in Proceedings of 14-th International Conference on Network-Based Information Systems NBiS 2011, Tirana, Albania, September 7- 9, 2011, pp. 186-193.
57) *Pawat Chomphoosang, *Ping Zhang, Arjan Durresi, Leonard Barolli, “Survey of Trust Based Communications in Social Networks,” in Proceedings of 14-th International Conference on Network-Based Information Systems NBiS 2011, Tirana, Albania, September 7- 9, 2011, pp. 663-666.
58) Arjan Durresi, Mimoza Durresi, Vamsi Paruchuri and Leonard Barolli, “Ad Hoc Communications for Emergency Conditions,” in Proceedings of the 25th IEEE International Conference on Advanced Information Networking and Applications AINA-2011, Biopolis, Singapore, March 22 - 25, 2011, pp. 787-794.
59) Tao Yang, Gjergji Mino, Evjola Spaho, Leonard Barolli, Arjan Durresi and Fatos Xhafa, “A Simulation System for Multi Mobile Events in Wireless Sensor Networks,” in Proceedings of the The 25th IEEE International Conference on Advanced Information Networking and Applications AINA-2011, Biopolis, Singapore, March 22 - 25, 2011, pp. 411-418.
60) Leonard Barolli, Hironori Ando, Fatos Xhafa, Arjan Durresi, Rozeta Miho and Akio Koyama, “Evaluation of an Intelligent Fuzzy-based Cluster Head Selection System for WSNs Using Different Parameters,” in Proceedings of the The 25th IEEE International Conference on Advanced Information Networking and Applications AINA-2011, Biopolis, Singapore, March 22 - 25, 2011, 388-395.
61) F. Li, X. Zou, P. Liu and Y. Chen, “New threats to health data privacy,” BMC Bioinformatics 2011, 12 (Suppl 12):S7 doi:10.1186/1471-2105-12-S12-S7
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62) *Pratima Adusumilli, *Yan Sui, Xukai Zou, Byrav Ramamurthy, and Feng Li, “A Key Distribution Scheme for Distributed Group with Authentication Capability,” International Journal of Performa- bility Engineering. 8(2), March 2012, pp. 179–192
63) X. Zou, P. Liu, and J. Chen, “Personal Genome Privacy Protection with Feature-based Hierarchical Dual-stage Encryption,” The 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS11), San Antonio, Texas, USA, Dec. 4-6. 2011.
64) *W. Peng, F. Li, X. Zou and J. Wu, “ Behavioral Detection and Containment of Proximity Malware in Delay Tolerant Networks,” The 8th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2011), October 17-22, 2011, Valencia, Spain, Pages 411-420.
65) X. Zou, F. Maino, E. Bertino, *Y. Sui, *K. Wang, and F. Li, “A New Approach to Weighted Multi-Secret Sharing,” ICCCN 2011, July 31 - August 4, Hawaii, USA.
66) *Y. Sui, X. Zou, and Y. Du, “Biometrics-based Authentication: a New Approach,” ICCCN 2011, July 31 - August 4, Hawaii, USA.
67) Xukai Zou, *Mingrui Qi, Feng Li, *Yan Sui, and *Kai Wang, “A New Scheme for Anonymous Secure Group Communication,” 44th Hawaii Int’l Conference on System Sciences (HICSS-44), Kauai, Hawaii, USA, January 4-7, 2011.
68) *Y. Wang, B, Ramamurthy, X. Zou, and Y. Xue, “ An efficient scheme for removing compromised sensor nodes from wireless sensor networks,” Security and Communication Networks, 3(4): 320-333 (2010).
69) F. Li, Y. Chen, X. Zou and P. Liu, “New Privacy Threats in Healthcare Informatics: When Medical Records Join the Web,” Proceedings of BIOKDD 2010, Washington DC, USA.
70) F. Li, Y. Yang, J. Wu and X. Zou, “Fuzzy Closeness-based Delegation Forwarding in Delay Tolerant Networks,” the 5th IEEE International Conference on Networking, Architecture, and Storage (NAS 2010), pp. 333–340.
71) *Y. Sui, K. Yang, Y. Du, S. Orr, and X. Zou, “ A novel key management scheme using biometrics,” Proceedings of Mobile Multimedia/Image Processing, Security, and Applications 2010 conference, 5–9 April 2010, Orlando, Florida, USA, Vol. 7708, 77080C (2010).
72) K. Yang, * Y. Sui, Z. Zhou, Y. Du, and X. Zou, “ A new approach for cancelable iris recognition,” Proceedings of Mobile Multimedia/Image Processing, Security, and Applications 2010 conference, 5–9 April 2010, Orlando, Florida, USA, Vol. 7708, 77080A (2010).
73) Vamsi Paruchuri, Arjan Durresi, “PAAVE: Protocol for Anonymous Authentication in Vehicular Networks using Smart Cards,” in Proceedings of IEEE Global Communications Conference - GLOBECOM 2010, Miami, USA, December 6-10, 2010, pp. 1-5.
74) Arjan Durresi, *Ping Zhang, Mimoza Durresi, Leonard Barolli, “Architecture for Mobile Heterogeneous Multi Domain Networks,” International Journal of Mobile Information Systems Vol. 6, No. 1, 2010, pp. 49-63.
75) Arjan Durresi, Mimoza Durresi, Zeynep Salih and Leonard Barolli, “Secure Hybrid Communications for Medical Applications,” in Proceedings of the Fifth International Conference on Broadband and Wireless Computing, Communication and Applications BWCCA-2010, Fukuoka, Japan, November 4-6, 2010, pp. 286-292.
76) Hironori Ando, Leonard Barolli, Arjan Durresi, Fatos Xhafa, and Akio Koyama, “An Intelligent Fuzzy-based Cluster Head Selection System for WSNs and its Performance Evaluation for D3N Parameter,” in Proceedings of the Fifth International Conference on Broadband and Wireless Computing, Communication and Applications BWCCA-2010, Fukuoka, Japan, November 4-6, 2010, pp. 648-653.
77) Taku Ikebata, Gjergji Mino, Leonard Barolli, Fatos Xhafa, Arjan Durresi and Akio Koyama, “Evaluation of a Fuzzy-based CAC Scheme for Different Priorities in Wireless Cellular Networks,” in Proceedings of the Fifth International Conference on Broadband and Wireless Computing, Communication and Applications BWCCA-2010, Fukuoka, Japan, November 4-6, 2010, pp. 616-621.
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78) *Ping Zhang, Arjan Durresi, Leonard Barolli, “Policy Based Mobility Support in Heterogeneous Networks,” in Proceedings of 13th International Conference on Network-Based Information Systems - NBiS 2010, Takayama, Japan, September 14-16, 2010, pp. 265-272.
79) Mukul Goyal, Sudeepa Prakash,Weigao Xie, Yusuf Bashir, Hossein Hosseini, Arjan Durresi,“Evaluating the Impact of Signal to Noise Ratio on IEEE 802.15.4 PHY-level Packet Loss Rate,” in Proceedings of the 13th International Conference on Network-Based Information Systems - NBiS 2010, Takayama, Japan, September 14-16, 2010, pp. 279 – 284.
80) Hironori Ando, Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama, “An Intelligent Fuzzy-based Cluster Head Selection System for Wireless Sensor Networks and Its Performance Evaluation, in Proceedings of the 13th International Conference on Network-Based Information Systems - NBiS 2010, Takayama, Japan, September 14-16, 2010, pp. 55-61.
81) Leonard Barolli, Makoto Ikeda, Fatos Xhafa, Arjan Durresi, “A Testbed for MANETs: Implementation, Experiences and Learned Lessons,” IEEE Systems Journal, Vol. 4, no. 2, June 2010, pp. 243–252.
82) Arjan Durresi, Leonard Barolli, Akio Koyama, Makoto Takizawa, “Ubiquitous QoS Communications using Scalable Satellite Networking ,” Journal of Ubiquitous Computing and Intelligence (JUCI), Vol. 6, No. 2, 2010, pp. 214–228.
83) Gjergj Mino, Leonard Barolli, Arjan Durresi, Akio Koyama, “Performance evaluation of a fuzzy-based CAC scheme for wireless cellular networks: a case study considering priority of on-going connections,” International Journal Business Intelligence and Data Mining, Vol. 5, No. 3, 2010, pp. 269–284.
84) Songnan Xi, Hsiao-Chun Wu, Tho Le-Ngoc, Arjan Durresi, “Fast channel estimation using Maximum-Length Shift-Register sequences,” International Journal of Wireless and Mobile Computing, Vol. 4, No. 2, 2010, pp. 148–152.
85) Leonard Barolli, Tao Yang, Fatos Xhafa, Arjan Durresi, “Routing Efficiency In Wireless Sensor-Actor Networks Considering Semi-Automated Architecture,” Journal of Mobile Multimedia, Vol. 6, No. 2, 2010, pp. 097–113.
86) Arjan Durresi, Vamsi Parachuri, “Adaptive Clustering Protocol for Wireless Networks,” in Proceedings of the 24th IEEE Advanced Information Networking and Applications AINA 2010, Perth, Australia, April 20-23, 2010, pp. 105–112.
87) Makoto Ikeda, Masahiro Hiyama, Leonard Barolli, Fatos Xhafa, Arjan Durresi, Makoto Takizawa, “Mobility Effects of Wireless Multi-hop Networks in Indoor Scenarios,” in Proceedings of the 24th IEEE Advanced Information Networking and Applications AINA 2010, Perth, Australia, April 20-23, 2010, pp. 495–503.
88) Weigao Xie, Mukul Goyal, Hossein Hosseini, Jerald Martocci, Yusuf Bashir, Emmanuel Baccelli, Arjan Durresi, “Routing Loops in DAG-based Low Power and Lossy Networks,” in Proceedings of the 24th IEEE Advanced Information Networking and Applications AINA 2010, Perth, Australia, April 20-23, 2010, pp. 888–896.
89) Tao Yang, Makoto Ikeda, Gjergji Mino, Leonard Barolli, Arjan Durresi, Fatos Xhafa, “Performance Evaluation of Wireless Sensor Networks for Mobile Sink Considering Consumed Energy Metric,” in Proceedings of the 24th IEEE Advanced Information Networking and Applications Workshops AINA 2010, Perth, Australia, April 20-23, 2010, pp. 245–250.
90) Arjan Durresi, Mimoza Durresi, Vamsi Paruchuri, Leonard Barolli, “Source Adaptive Receiver Driven Layered Multicast,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, Krakow, Poland, February 15 - 18, 2010, pp. 201-208.
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91) Tao Yang, Makoto Ikeda, Leonard Barolli, Arjan Durresi, Fatos Xhafa, “Performance Evaluation of Wireless Sensor Networks for Different Radio Models Considering Mobile Event,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, Krakow, Poland, February 15 - 18, 2010, pp. 180–187.
92) Makoto Ikeda, Masahiro Hiyama, Leonard Barolli, Fatos Xhafa, Arjan Durresi, “Mobility Effects on the Performance of Mobile Ad hoc Networks,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, Krakow, Poland, February 15 - 18, 2010, pp. 230–237.
93) *Y. Sui, F. Maino, Y. Guo, * K. Wang and X. Zou, An Efficient Time-bound Access Control Scheme for Dynamic Access Hierarchy, in Proceedings of The Fifth International Conference on Mobile Ad-hoc and Sensor Networks (MSN 2009), 14-16 December 2009, Wu Yi Mountain, China, pp.279 - 286.
94) *K. Wang, X. Zou, and Y. Sui*, A Multiple Secret Sharing Scheme based on Matrix Projection, COMPSAC’09, Seattle, WA, USA, July 20 –24, 2009, pp. 400-405.
95) B. Li and X. Zou, A Proactive Secret Sharing Scheme in Matrix Projection Method, International Journal of Security and Networks, 4(4), 2009, pp. 201-209.
96) Y. Dai, X. Li, X. Zou, and L. Xing, Rebound Wall: A Novel Technology against DoS Attacks, Special Issue on System Survivability and Defense against External Impacts, International Journal of Performability Engineering, Vol. 5, No. 1, pp. 55-70, January 2009.
97) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, Rajgopal Kannan “Anonymous communication protocol for sensor networks,” International Journal of Wireless and Mobile Computing (IJWMC), Vol. 3, No. 4, 2009, pp. 236–246.
98) Leonard Barolli, Akio Koyama, Yoshitaka Honma, Arjan Durresi, Junpei Arai, “Performance evaluation of selective-border-casting zone routing protocol for ad-hoc networks,” International Journal of Wireless and Mobile Computing (IJWMC), Vol. 3, No. 4, 2009, pp. 312–319.
99) Tao Yang, Leonard Barolli, Makoto Ikeda, Giuseppe De Marco, Arjan Durresi, “Performance Evaluation of a Wireless Sensor Network for Mobile and Stationary Event Cases Considering Routing Efficiency and Goodput Metrics”, Journal of Scalable Computing: Practice and Experience (SCPE), Vol. 10, No. 1, 2009, pp. 99–109.
100) Arjan Durresi, Mimoza Durresi, Leonard Barolli, and Fatos Xhafa, “MPLS Traffic Engineering for Multimedia on Satellite Networks,” Journal of Mobile Multimedia JMM, Vol. 5, No. 1, 2009, pp. 3–11.
101) Makoto Ikeda, Leonard Barolli, Giuseppe De Marco, Tao Yang, Arjan Durresi, Fatos Xhafa, “Tools for performance assessment of OLSR protocol,” Mobile Information Systems, Vol. 5, No. 2, 2009, pp. 165–176.
102) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “FAST: Fast Autonomous System Traceback,” Journal of Network and Computer Applications, Vol. 32, No. 2, 2009, pp. 448–454.
103) Arjan Durresi, Vamsi Paruchuri, Mimoza Durresi, Leonard Barolli, Makoto Takizawa, “A scalable anonymous protocol for heterogeneous wireless ad hoc networks,” Journal of Embedded Computing, Vol. 3, No. 1, 2009, pp. 77–85.
104) Arjan Durresi, Vamsi Paruchuri, “Secure Communication among Cell Phones and Sensor Networks,” in Proceedings of IEEE Global Communications Conference - GLOBECOM 2009, Honolulu, Hawaii, USA, November 30 - December 4, 2009, pp. 167–174.
105) Arjan Durresi, Mimoza Durresi, Vamsi Paruchuri, Leonard Barolli, “Trust Management in Emergency Networks,” in Proceedings of The 23rd IEEE International Conference on Advanced Information Networking and Applications (AINA-09) University of Bradford, Bradford, UK, May 26-29, 2009, pp. 167–174.
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106) Leonard Barolli, Makoto Ikeda, Giuseppe De Marco, Arjan Durresi, Fatos Xhafa, “Performance Analysis of OLSR and BATMAN Protocols Considering Link Quality Parameter,” in Proceedings of The 23rd IEEE International Conference on Advanced Information Networking and Applications (AINA-09) University of Bradford, Bradford, UK, May 26-29, 2009, pp. 307–314.
107) Keita Matsuo, Leonard Barolli, Fatos Xhafa, Vladi Kolici, Akio Koyama, Arjan Durresi, Rozeta Miho, “Implementation of an E-learning System Using P2P,Web and Sensor Technologies,” in Proceedings of The 23rd IEEE International Conference on Advanced Information Networking and Applications (AINA-09) University of Bradford, Bradford, UK, May 26-29, 2009, pp. 800–807.
108) *Ping Zhang, Arjan Durresi, Leonard Barolli, “A Survey of Internet Mobility,” in Proceedings of the 12th International Conference on Network-Based Information Systems – NbiS 2009, IUPUI, Indianapolis, USA, August 19-21, 2009, pp. 147–154.
109) Makoto Ikeda, Leonard Barolli, Masahiro Hiyama, Tao Yang, Giuseppe De Marco, Arjan Durresi, “Performance Evaluation of a MANET Tested for Different Topologies,” in Proceedings of the 12th International Conference on Network-Based Information Systems – NbiS 2009, IUPUI, Indianapolis, USA, August 19-21, 2009, pp. 327–334.
110) Tao Yang, Leonard Barolli, Makoto Ikeda, Fatos Xhafa, Arjan Durresi, “Performance Analysis of OLSR Protocol for Wireless Sensor Networks and Comparison Evaluation with AODV Protocol,” in Proceedings of the 12th International Conference on Network-Based Information Systems - NBiS 2009, IUPUI, Indianapolis, USA, August 19-21, 2009, pp. 335–342.
111) Keita Matsuo, Leonard Barolli, Joan Arnedo-Moreno, Fatos Xhafa, Akio Koyama, Arjan Durresi, “Experimental Results and Evaluation of SmartBox Stimulation Device in a P2P E-learning System,” in Proceedings of the 12th International Conference on Network-Based Information Systems - NBiS 2009, IUPUI, Indianapolis, USA, August 19-21, 2009, pp. 37–44.
112) Gjergji Mino, Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama, “A Comparison Study of Two Fuzzy-Based Handover Systems for Avoiding Ping-Pong Effect in Wireless Cellular Networks,” in Proceedings of the 12th International Conference on Network-Based Information Systems - NBiS 2009, IUPUI, Indianapolis, USA, August 19-21, 2009, pp. 564–571.
113) *Ping Zhang, Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Mobility over Heterogeneous Multi Domain Networks,” in Proceedings of the 11th International Workshop on Multimedia Network Systems and Applications - MNSA 2009, held in conjunction with IEEE ICDCS 2009, Montreal, Quebec, Canada, June 22-26, 2009, pp. 388–394.
114) Gjergji Mino, Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama “A Fuzzy-Based Call Admission Control Scheme for Wireless Cellular Networks Considering Priority of Ongoing Connections,” in Proceedings of the 11th International Workshop on Multimedia Network Systems and Applications - MNSA 2009, held in conjunction with IEEE ICDCS 2009, Montreal, Quebec, Canada, June 22-26, 2009, pp. 380–387.
115) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Heterogeneous Multi Domain Network Architecture for Military Communications,” in Proceedings of the Third International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2009), Fukuoka, Japan, March 16-19, 2009, pp. 382–387.
116) *Birhan Payli, Arjan Durresi, U. Deniz Dincer, Leonard Barolli, “Real-time Monitoring of Vital Signs,” in Proceedings of The Second IEEE International Workshop on Bio Computing (BioCom-09), held in conjunction with IEEE AINA 2009, University of Bradford, Bradford, UK, May 26-29, 2009, pp. 1013–1018.
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117) Makoto Ikeda, Leonard Barolli, Masahiro Hiyama, Giuseppe De Marco, Tao Yang, Arjan Durresi, “Performance Evaluation of Link Quality Extension in Multihop Wireless Mobile Adhoc Networks,” in Proceedings of the Third International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2009), Fukuoka, Japan, March 16-19, 2009, pp. 311–318.
118) Keita Matsuo, Leonard Barolli, Vladi Kolici, Fatos Xhafa, Akio Koyama, Arjan Durresi, “Stimulation Effects of SmartBox for E-learning Using JXTA-Overlay P2P System,” in Proceedings of the Third International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2009), Fukuoka, Japan, March 16-19, 2009, pp. 231–238.
119) X. Zou and Li Bai, “A New Class Of Key Management Scheme For Access Control In Dynamic Hierarchies,” International Journal of Computer and Applications, 30(4), 2008, pp. 331-337.
120) S. Magliveras, W. Wei, and X. Zou, “Notes on the CRTDH Group Key Agreement Protocol,” Proceedings of The 28th International Conference on Distributed Computing Systems Workshops (ICDCS’08), Beijing, China, June 17–20, 2008, pp. 406-411. (Note: the authors are listed in the alphabetic order of their last names.)
121) X. Zou, Y. Dai and E. Bertino, “A Practical and Flexible Key Management Mechanism For Trusted Collaborative Computing,” Proceedings of the 27th IEEE INFOCOM, April 13–18, 2008, pp. 1211– 1219.
122) Y. Wang, B. Ramamurthy, Y. Xue, and X. Zou, “A Security Framework for Wireless Sensor Networks Utilizing a Unique Session Key,” The 5th International Conference on Broadband Communications, Networks and Systems, 2008 (BROADNETS 2008), pp. 487 - 494.
123) X. Zou and * Y. karandikar, “A Novel Conference Key Management solution for Secure Dynamic Conferencing,” Inter. Journal of Security and Networks, 3(1), 2008, pp. 47–53.
124) R. Balachandran, X. Zou, B. Ramamurthy, * A. Thurkral, N. V. Vinodchandran, “ An Efficient and Attack-resistant Key Agreement Scheme for Secure Group Communications in Mobile Ad-Hoc Networks,” Wireless Communications & Mobile Computing. 8(10), 2008, pp. 1297-1312.
125) *K. Wang, * Y. Sui, X. Zou, A. Durresi, and S. Fang, “ Pervasive and Trustworthy Healthcare.” The First IEEE International Workshop on Bio Computing (BioCom’08), Okinawa, Japan, March 25 - 28, 2008, pp. 750–755.
126) X. Zou, Y. Dai and Y. Pan, “Trust and Security in Collaborative Computing”, World Scientific, ISBN-13: 978-981-270-3682, January 2008, pages 242.
127) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “Network Adaptive Layered Multicast for Heterogeneous Wireless,” Journal of Mobile Multimedia JMM, Vol. 4, No. 1, 2008, pp. 071-082.
128) Arjan Durresi, Vamsi Paruchuri,“Adaptive Backbone Protocol for Heterogeneous Wireless Networks,” Journal of Telecommunication Systems: Springer, Vol. 38, No. 3-4, 2008, pp. 83–97.
129) Arjan Durresi, “Routing of Real-time Traffic over a Multilayered Satellite Architecture,” International Journal of Wireless and Mobile Computing (IJWMC), Vol. 3, No. 1/2, 2008, pp. 22–34.
130) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Secure Authentication in Heterogeneous Wireless Networks,” International Journal of Mobile Information Systems, Vol. 4, No. 2, 2008, pp. 119–130.
131) Arjan Durresi, Mimoza Durresi, Arben Merkoci, Leonard Barolli, “Networked Biomedical System for Ubiquitous Health Monitoring,” International Journal of Mobile Information Systems, Vol. 4, No. 3, 2008, pp. 211–218.
132) Leonard Barolli, Junpei Anno, Fatos Xhafa, Arjan Durresi, Akio Koyama, “A Context-Aware Fuzzy-Based Handover System for Wireless Cellular Network and its Performance Evaluation,” Journal of Mobile Multimedia JMM, Vol.4 No.3/4 October 1, 2008, pp. 241–258.
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133) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Priority Based Wireless Communications for Health Monitoring on Highways,” Journal of Interconnection Networks, Vol. 9, No. 4, 2008, pp. 337–349.
134) Junpei Anno, Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama, “Performance evaluation of two fuzzy-based cluster head selection systems for wireless sensor networks,” Journal of Mobile Information Systems, Vol. 4, No. 4, 2008, pp. 2977–312.
135) Keita Matsuo, Leonard Barolli, Fatos Xhafa, Akio Koyama, Arjan Durresi, “New Functions for Stimulating Learners Motivation in a Web-Bbased E-Learning System,” Journal of Distance Education Technologies, Vol. 6, No. 4, October-December 2008, pp. 34–49.
136) Kaoru Sugita, Noriki Uchida, Giuseppe De Marco, Leonard Barolli, Arjan Durresi, “Performance Evaluation of WWW Conference System for Supporting Remote Mental Health Care Education,” International Journal of Virtual Technology and Multimedia (IJVTM), Vol. 1, No. 1, 2008, pp. 75–93.
137) Arjan Durresi, “Spatial Authentication Using Cell Phones,” in Handbook of Research on Information Security and Assurance, Edited by Jatinder N. D. Gupta and Sushil K. Sharma, Published by Information Science Reference, ISBN-10: 1599048558, July 16, 2008.
138) Arjan Durresi and Raj Jain, “ATM Networks,” Invited Chapter, in The Handbook of Computer Networks, Edited by Hossein Bidgoli, John Wiley & Sons Inc., ISBN-13: 978- 0471784616, November 28, 2007.
139) Leonard Barolli, Tao Yang, Makoto Ikeda, Arjan Durresi, Fatos Xhafa, “A Simulation System for Routing Efficiency in Wireless Sensor-Actor Networks: A Case Study for Semiautomated Architecture,” in Proceedings of the 14th Intl Conference on Parallel and Distributed Systems ICPADS’08, Melbourne, Victoria, Australia, December 8th-10th, 2008, pp. 567–574.
140) Vamsi Paruchuri, Arjan Durresi, Sriram Chellappan, “TTL based Packet Marking for IP Traceback,” in Proceedings of GLOBECOM 2008, New Orleans, LA, November 30 – December 4, 2008, pp. 1–5.89.
141) Vladi Kolici, Keita Matsuo, Leonard Barolli, Fatos Xhafa, Arjan Durresi, Rozeta Miho, “A P2P System Based on JXTA-Overlay and Its Application for End-Device Control,” in Proceedings of The 6th International Conference on Advances in Mobile Computing & Multimedia MoMM2008, Linz, Austria, November 26, 2008, pp. 364–369. Best Paper Award.
142) Vamsi Paruchuri, Arjan Durresi, Sriram Chellappan, “Secure Communications over Hybrid Military Networks,” in Proceedings of MILCOM 2008, San Diego, CA, November 17-19, 2008, pp. 1–7.
143) Sriram Chellappan, Vamsi Paruchuri, Dylan McDonald, Arjan Durresi, “Localizing Sensors in Un-Friendlly Environments,” in Proceedings of MILCOM 2008, San Diego, CA, November 17-19, 2008, pp. 1–7.
144) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Secure Ubiquitous Health Monitoring System,” in Proceedings of The International Conference on Network-Based Information Systems NBiS-2008, Turin, Italy, September 1-5, 2008, pp. 273–282
145) Makoto Ikeda, Leonard Barolli, Giuseppe De Marco, Tao Yang, Arjan Durresi, “Experimental and Simulation Evaluation of OLSR Protocol for Mobile Ad-Hoc Networks,” in Proceedings of The International Conference on Network-Based Information Systems NBiS-2008, Turin, Italy on September 1-5, 2008, pp. 111–121.
146) Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama, “A Fuzzy-Based Handover System for Wireless Cellular Networks: A Case Study for Handover Enforcement,” in Proceedings of The International Conference on Network-Based Information Systems NBiS-2008, Turin, Italy on September 1-5, 2008, pp. 212–222.
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147) Leonard Barolli, Makoto Ikeda, Arjan Durresi, Fatos Xhafa, Akio Koyama, “Performance Evaluation of Two Search Space Reduction Methods for a Distributed Network Architecture,” in Proceedings of The International Conference on Network-Based Information Systems NBiS-2008, Turin, Italy on September 1-5, 2008, pp. 49–59
148) Junpei Anno, Leonard Barolli, Arjan Durresi, Fatos Xhafa, Akio Koyama, “A cluster head decision system for sensor networks using fuzzy logic and number of neighbor nodes,” in the Proceedings of The First IEEE International Conference on Ubi-media Computing, Lanzhou University, China, July 15 - 16, 2008, pp. 50-56.
149) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Security of Mobile and Heterogeneous Wireless Networks in Battlefields,” in Proceedings of The International Workshop on Next Generation of Wireless and Mobile Networks, held in conjunction with ICPP 2008, Portland, Oregon, USA, September 8-12, 2008, pp. 167–172.
150) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “Adaptive Layered Multicast for Heterogeneous Wireless Networks,” in Proceedings of the 10th International Workshop on Multimedia Network Systems and Applications MNSA-2008, held in conjunction with ICDCS 2008, Beijing, China June 17 - 20, 2008.
151) Tao Yang, Makoto Ikeda, Giuseppe De Marco, Leonard Barolli, Arjan Durresi, Fatos Xhafa, “Routing Efficiency of AODV and DSR in Ad-Hoc Sensor Networks,” in Proceedings of the 10th International Workshop on Multimedia Network Systems and Applications MNSA-2008, held in conjunction with ICDCS 2008, Beijing, China June 17 - 20, 2008.
152) Vamsi Paruchuri, Arjan Durresi, Ramesh M, “Securing Powerline Communications,” in Proceedings of The IEEE International Symposium on Power Line Communications and Its Applications ISPLC, April 2-4, 2008, pp. 64–69.
153) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “Hybrid Stealth Communication Protocol,” in Proceedings of The 22nd IEEE International Conference on Advanced Information Networking and Applications AINA 2008, Ginowan, Okinawa, Japan, on March 25-28, 2008, pp. 606–611.
154) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Secure Mobile Communications for Battlefields,” in Proceedings of The International Conference on Complex, Intelligent and Software Intensive Systems CISIS-2008, Technical University of Catalonia, Barcelona, Spain, March 4-7, 2008.
155) Keita Matsuo, Leonard Barolli, Fatos Xhafa, Akio Koyama, Arjan Durresi, Makoto Takizawa, “Implementation and Design of New Functions for a Web-Based E-learning System to Stimulate Learners Motivation,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems CISIS-2008, Barcelona, Spain, March 4 - 7, 2008, pp. 513–518.
156) Leonard Barolli, Fatos Xhafa, Arjan Durresi, Akio Koyama, Makoto Takizawa, “An Intelligent Handoff System for Wireless Cellular Networks Using Fuzzy Logic and Random Walk Model,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems CISIS-2008, Barcelona, Spain, March 4 - 7, 2008, pp. 5–11.
157) Arjan Durresi, Mimoza Durresi, Leonard Barolli: “Secure Spatial Authentication for Mobile Stations In Hybrid 3G-WLAN Serving Networks,” in Proceedings of the Third International Conference on Availability, Reliability and Security, ARES 2008, Technical University of Catalonia, Barcelona, Spain, March 4-7, 2008, pp. 1325–1331.
158) Keita Matsuo, Leonard Barolli, Fatos Xhafa, Akio Koyama, Arjan Durresi, Makoto Takizawa, “Design and Implementation of a JXTA-Overlay P2P System and Smart Box Environment,” in Proceedings of AINA Workshops 2008, Ginowan, Okinawa, Japan, March 25-28, 2008, pp. 407–412.
159) X. Zou, Y.S. Dai, B. Doebbeling,* M. Qi, “Dependability and Security in Medical Information
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System,” the 12th International Conference on Human-Computer Interaction, 2007, Lecture Notes of Computer Science (LNCS), vol. 4553, pp. 316-326.
160) X. Zou, * Y. karandikar, E. Bertino, “ A Dynamic Key Management Solution to Access Hierarchy,” International Journal of Network Management, 17(6), 2007, pp. 437–450.
161) X. Zou, Y. Dai, and X. Ran, “ Dual-Level Key Management for Secure Grid Communication in Dynamic and Hierarchical Groups”, Future Generation of Computer Systems, 23(6), 2007, pp. 776–786.
162) Y. Dai, Y. Pan, and X. Zou, “A Hierarchical Modeling and Analysis for Grid Service Reliability,” IEEE Transaction on Computers, 56(5), 2007, pp. 681–691.
163) Y. Wang, B. Ramamurthy, and X. Zou, “KeyRev: An Efficient Key Revocation Scheme for Wireless Sensor Networks,” Proceedings of IEEE ICC’07, 24-27 June 2007, Glasgow, Scotland, pp.1260 - 1265.
164) Arjan Durresi, Mukundan Sridharan, Raj Jain, “Adaptive Multi-level Explicit Congestion Notification,” International Journal of High performance Computing and Networking (IJHPCN), Vol. 5, No. 1/2, October 2007, pp. 3–11.
165) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Secure Broadcast for Inter Vehicle Communications,” International Journal of High performance Computing and Networking (IJHPCN), Vol. 5, No. 1/2, October 2007, pp. 54–61.
166) Arjan Durresi, Vamsi Paruchuri, “Broadcast Protocol for Energy-Constrained Networks,” IEEE Transaction on Broadcasting, Vol. 53, No. 1, 2007, pp. 112–119. (impact factor 2.248)
167) Fatos Xhafa, Leonard Barolli, Arjan Durresi, “Requirements for an Event-Based Simulation Package for Grid Systems,” Journal of Interconnection Networks (JOIN), Vol. 8, No. 2, 2007, pp. 163–178.
168) Tao Yang, Leonard Barolli, Makoto Ikeda, Arjan Durresi, Fatos Xhafa, “Performance Evaluation Of Reactive And Proactive Protocols For Ad-Hoc Sensor Networks Using Different Radio Models,” Journal of Interconnection Networks (JOIN), Vol. 8, No. 4, 2007, pp. 387–405.
169) Fatos Xhafa, Leonard Barolli, Arjan Durresi, “An Experimental Study On Genetic Algorithms for Resource Allocation On Grid Systems,” Journal of Interconnection Networks (JOIN), Vol. 8, No. 4, 2007, pp. 427–443.
170) Akio Koyama, Junpei Arai, Leonard Barolli, Arjan Durresi, “EZRP: An Enhanced Zone-Based Routing Protocol for Ad-Hoc Networks and Its Performance Evaluation,” Concurrency and Computation: Practice and Experience, Wiley InterScience, Vol. 19, No. 8, June 10, 2007, pp. 1141–1156.
171) Tao Yang, Makoto Ikeda, Leonard Barolli, Arjan Durresi and Fatos Xhafa, “Network Energy Consumption in Ad-hoc Networks Under Different Radio Models,” in Proceedings of The 13th International Conference on Parallel and Distributed Systems (ICPADS 2007), Hsinchu, Taiwan, December 5-7, 2007.
172) Leonard Barolli, Fatos Xhafa, Arjan Durresi, Akio Koyama, Makoto Takizawa, “Performance Evaluation of a Fuzzy-based Handover System for Wireless Cellular Networks,” in Proceedings of The 9th International Conference on Information Integration and Web-based Application & Services (iiWAS 2007), Jakarta, Indonesia, December 3-5, 2007.
173) Vamsi Paruchuri, Arjan Durresi, Raj jain, “On the (in)effectiveness of Probabilistic Marking for IP Traceback under DDoS Attacks,” in Proceedings of the 50th IEEE Global Telecommunications Conference GLOBECOM 2007, Washington, D.C., on November 26-30, 2007.
174) Vamsi Paruchuri, Arjan Durresi, Leonard Barolli, Makoto Takizawa, “Three Dimensional Broadcast Protocol for Wireless Networks,” in Proceedings of the International
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Conference on Parallel Processing (ICPP 2007), XiAn China, September 10-14, 2007, pp. 68.
175) Arjan Durresi, Arben Merkoci, Mimoza Durresi, Leonard Barolli, “Integrated Biomedical System for ubiquitous Health Monitoring,” in Proceedings of the International Conference on Network-Based Information Systems - NBiS2007, Regensburg, Germany, 3-7 September 2007.
176) Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Wireless Communications for Health Monitoring on Highways,” in Proceedings of the Ninth IEEE International Workshop on Multimedia Network Systems and Applications MNSA-2007, held in conjunction with The 27th 16 IEEE International Conference on Distributed Computing Systems ICDCS-2007, Toronto, Canada, June 25-29, 2007.
177) Leonard Barolli, Fatos Xhafa, Arjan Durresi, Akio Koyama, “A Fuzzy-based Call Admission Control Scheme for Wireless Cellular Networks,” in Proceedings of The Ninth IEEE International Workshop on Multimedia Network Systems and Applications MNSA-2007, held in conjunction with The 27th IEEE International Conference on Distributed Computing Systems ICDCS-2007, in Toronto, Canada, June 25-29, 2007.
178) Arjan Durresi, Vamsi Paruchuri, “Adaptive Coordination Protocol for Heterogeneous Wireless Networks,” in Proceedings of the IEEE International Conference on Communications-ICC 2007, Glasgow, Scotland, June 24-28, 2007, pp. 4805–4810.
179) Arjan Durresi, “Anonymity in the Internet,” Cluster Computing: The Journal of Networks, Software Tools and Applications, Springer, Vol. 10, No. 1, March 24, 2007, pp. 57–66.
180) Arjan Durresi, Leonard Barolli, Raj Jain, Makoto Takizawa, “Congestion Control using Multilevel Explicit Congestion Notification,” Journal of Information Processing Society of Japan, Vol. 48, No. 2, February 2007, pp. 42–54.
181) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “Anonymous Routing for Mobile Wireless Ad Hoc Networks,” International Journal of Distributed Sensor Networks, Vol. 3. No. 1, 2007, pp. 105–117.
182) Arjan Durresi, Vijay Bulusu, Vamsi Paruchuri, Leonard Barolli, “Secure Emergency Communication of Cellular Phones in Ad Hoc Mode,” Ad Hoc Networks Journal, Vol. 5, No. 1, January 2007, pp. 126–133.
183) Fatos Xhafa, Leonard Barolli, Arjan Durresi, “Batch Mode Scheduling in Grid Systems,” International Journal of Web and Grid Services (IJWGS), Vol. 3, No. 1, 2007, pp. 19–37.
184) Fatos Xhafa, Leonard Barolli, Arjan Durresi, “Immediate Mode Scheduling in Grid Systems,” Journal of Web and Grid Services, Vol.3, No.2, 2007, pp. 219-236.
185) Leonard Barolli, Makoto Ikeda, Giuseppe De Marco, Arjan Durresi, Akio Koyama, Jiro Iwashige, “A Search Space Reduction Algorithm for Improving the Performance of a GAbased QoS Routing Method in Ad-Hoc Networks,” International Journal of Distributed Sensor Networks, Vol. 3. No. 1, 2007, pp. 41–57.
186) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “FAST: Fast Autonomous System Traceback,” in Proceedings of The IEEE 21st International Conference on Advanced Information Networking and Applications AINA 2007, Niagara Falls, CA, May 21-23, 2007, pp. 498 – 505.
187) Mimoza Durresi, Arjan Durresi, Leonard Barolli, “Herarchical Communications for Battlefields,” in Proceedings of the Third IEEE Workshop on Heterogeneous Wireless Networks - HWISE 2007, held in conjunction with AINA 2007, Niagara Falls, CA, May 21-23, 2007, pp. 690 – 695.
188) Fatos Xhafa, Leonard Barolli, Arjan Durresi, “Immediate Mode Scheduling of Independent Jobs in Computational Grids,” in Proceedings of The 21st IEEE International Conference on Advanced Information Networking and Applications AINA 2007, Niagara Falls, CA, May 21-23, 2007, pp. 970 – 977.
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189) Arjan Durresi, Vamsi Paruchuri, Leonard Barolli, “Secure Spatial Authentication using Cell Phones,” in Proceedings of the International Conference on Availability, Reliability and Security - ARES 2007, Vienna, Austria, April 10 - 13, 2007, pp. 543–549.
190) Arjan Durresi, Mimoza Durresi, Fatos Xhafa, Leonard Barolli, “MPLS Traffic Engineering in Satellite Networks,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems CISIS-2007, Vienna, Austria, April 10 - 13, 2007, pp. 19–26.
191) Leonard Barolli, Makoto Ikeda, Arjan Durresi, Fatos Xhafa, Akio Koyama, “A Distributed QoS Routing and CAC Framework: Performance Evaluation of Its SSRA and InterD Agents,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems CISIS-2007, Vienna, Austria, April 10 - 13, 2007, pp. 60–67.
192) Santi Caballe, Fatos Xhafa, Thanasis Daradoumis, Joan Esteve, Leonard Barolli, Arjan Durresi, “Using a Grid Platform for Enabling Real Time User Modeling in On-line Campus,” in Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems CISIS-2007, Vienna, Austria, April 10 - 13, 2007, pp. 35–42.
193) M. Navarro*, Y. Li*, and Y. Liang, Energy Profile for Environmental Monitoring Wireless Sensor Networks, IEEE Colombian Conference on Communications and Computing, 6 pages, Bogota, Colombia, June 4-6, 2014.
194) Yao Liang, Yimei Li*, An Efficient and Robust Data Compression Algorithm in Wireless Sensor Networks, IEEE Communications Letters, Vol. 18, No. 3, pp. 439-442, March 2014.
195) M. Navarro*, T. W. Davis*, Y. Liang, and X. Liang, A Study of Long-Term WSN Deployment for Environmental Monitoring, Proc. 24th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 2098-2102, London, UK, Sept. 8-11, 2013.
196) Yao Liang, and Rui Liu*, Routing Topology Inference for Wireless Sensor Networks, ACM SIGACOMM Computer Communication Review, Vol. 43, No. 2, pp. 22-27, April 2013.
197) Yao Liang, and Rui Liu*, Compressed Topology Tomography in Sensor Networks, Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 1339-1344, Shanghai, China, April 7 - 10, 2013.
198) Newlyn Erratt*, and Yao Liang, The Design and Implementation of A General WSN Gateway for Data Collection, Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 4439-4444, Shanghai, China, April 7 - 10, 2013.
199) W. Zhao* and Y. Liang, Inference in Wireless Sensor Networks Based on Information Structure Optimization, Proc. 37th IEEE Conference on Local Computer Networks (LCN), pp. 555-562, Clearwater, USA, Oct. 22-25, 2012.
200) T. Davis*, X. Liang, C.-M. Kuo*, and Y. Liang, Analysis of Power Characteristics for Sap Flow, Soil Moisture and Soil Water Potential Sensors in Wireless Sensor Networking Systems, IEEE Sensors Journal, Vol. 12, No. 6, pp. 1933-1945, 2012.
201) Daniel Salas*, Xu Liang, and Yao Liang, A Systematic Approach for Hydrological Model Couplings, International Journal of Communications, Network and System Sciences, Vol. 5, No. 6, pp. 343-352, doi:10.4236/ijcns.2012.56045, 2012.
202) T. Davis*, X. Liang, M. Navarro*, D. Bhatnagar*, and Y. Liang, An Experimental Study of WSN Power Efficiency: MICAz networks with XMesh, International Journal of Distributed Sensor Networks, Vol. 2012, doi: 10.1155/2012/358238, 14 pages, 2012.
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203) N. Erratt*, and Y. Liang, Compressed Data-Stream Protocol: An Energy-Efficient Compressed Data-Stream Protocol for Wireless Sensor Networks, IET Communications, Vol. 5, Iss. 18, pp.2673-2683, 2011.
204) Miguel Navarro*, Diviyansh Bhatnagar*, and Yao Liang, An Integrated Network and Data Management System for Heterogeneous WSNs, The Eighth IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), pp. 819-824, Valencia, Spain, Oct. 17-22, 2011.
205) Miguel Navarro*, Diviyansh Bhatnagar*, Rui Liu*, and Yao Liang, Design and Implementation of An Integrated Network and Data Management System for Heterogeneous WSNs, The Eighth IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), pp. 176-178 (demo paper), Valencia, Spain, Oct. 17-22, 2011.
206) Yao Liang, Efficient Temporal Compression in Wireless Sensor Networks, The 36th IEEE Conference on Local Computer Networks (LCN), pp. 470-478, Bonn, Germany, Oct. 4-7, 2011.
207) Wei Zhao*, and Yao Liang, Kernel-Based Markov Random Fields Learning for Wireless Sensor Networks, The 36th IEEE Conference on Local Computer Networks (LCN), pp. 155-158, Bonn, Germany, Oct. 4-7, 2011.
208) Megan Ayers* and Yao Liang, Gureen Game: An Energy-Efficient QoS Control Scheme for Wireless Sensor Networks, The Second International Green Computing Conference (IGCC'11), (8 pages), Orlando, Florida, USA, July 25-28, 2011.
209) Nimmy Ravindran*, Yao Liang, and Xu Liang, A Labeled-tree Approach to Semantic and Structural Data Interoperability Applied in Hydrology Domain, Information Sciences, Vol. 180, pp. 5008 – 5028, 2010.
210) Zhuotong Nan, Shugong Wang*, Xu Liang, Thomas E. Adams, William Teng, and Yao Liang, Analysis of spatial similarities between NEXRAD Stage III and LDAS Combo precipitation data products, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 3, No. 3, pp. 371 – 385, 2010.
211) Mei Han* and Yao Liang, A Virtual Queue Based Scheme to Support Real-Time Renegotiated VBR Video Streaming, International Transactions on Systems Science and Applications, Vol. 6, No. 1, pp. 60 – 72, 2010.
212) Wei Zhao* and Yao Liang, A Systematic Probabilistic Approach to Energy-Efficient and Robust Data Collections in Wireless Sensor Networks, International Journal of Sensor Networks (special issue on data quality management in wireless sensor networks), Vol. 7, No. 3, pp. 162 – 175, 2010.
213) Yao Liang and Wei Peng*, Minimizing Energy Consumptions in Wireless Sensor Networks via Two-Modal Transmission, ACM SIGACOMM Computer Communication Review, Vol. 40, No. 1, pp. 13 – 18, January 2010.
214) Paolo Pagano, Mangesh Chitnis*, Giuseppe Lipari, Christian Nastasi*, and Yao Liang, Simulating Real-Time Aspects of Wireless Sensor Networks, EURASIP Journal on Wireless Communications and Networking, Vol. 2010, Article ID 107946, 19 pages, doi:10.1155/2010/107946, 2010.
215) Arnold P. Boedihardjo* and Yao Liang, Hierarchical Smoothed Round Robin Scheduling in High-Speed Networks, IET Communications, Vol. 3, No. 9, pp. 1557 – 1568, 2009.
216) Mangesh Chitnis*, Yao Liang, Jiang Yu Zheng, Paolo Pagano, Giuseppe
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Lipari, Wireless Line Sensor Network for Distributed Visual Surveillance, The Sixth ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, pp. 71 – 78, Tenerife, Canary Islands, Spain, Oct. 26-30, 2009.
217) Wei Zhao*, Yao Liang, Qun Yu*, and Yan Sui*, H-WSNMS: A Web-Based Heterogeneous Wireless Sensor Networks Management System Architecture, Proc. 12th International Conference on Network-Based Information Systems (NBiS 2009), pp. 155 – 162, Indiana, USA, Aug. 19-21, 2009.
218) Qifeng Lu*, and Yao Liang, Multiresolution Learning on Neural Network Classifiers: A Systematic Approach, Proc. 12th International Conference on Network-Based Information Systems (NBiS 2009), pp. 505 – 511, Indiana, USA, Aug. 19-21, 2009.
219) Mangesh Chitnis*, Paolo Pagano, Giuseppe Lipari, and Yao Liang, A Survey on Bandwidth Resource Allocation and Scheduling in Wireless Sensor Networks, Proc. 12th International Conference on Network-Based Information Systems (NBiS 2009), pp. 121 – 128, Indiana, USA, Aug. 19-21, 2009.
220) Wei Zhao*, and Yao Liang, W-LBP: Wavelet-based Loopy Belief Propagation for Wireless Sensor Networks, Proc. Third International Conference on Sensor Technologies and Applications (SENSORCOMM), pp. 617 – 622, Athens, Greece, June 18-23, 2009.
221) Paolo Pagano, Francesco Piga*, and Yao Liang, Real-time Multi-View Vision Systems using WSNs, Proc. 24th Annual ACM Symposium on Applied Computing, pp. 2191 – 2196, Hawaii, USA, March 9 - 12, 2009.
222) Paolo Pagano, Francesco Piga*, Giuseppe Lipari, and Yao Liang, Visual tracking using Sensor Networks, Proc. Second International Conference on Simulation Tools and Techniques (SIMUTools), Rome, Italy, March 3-5, 2009 (10 pages).
223) Paolo Pagano, Christian Nastasi*, and Yao Liang, The multivision problem for Wireless Sensor Networks: a discussion about node and network architecture, Proc. International Workshop on Cyber-Physical Systems Challenges and Applications (CPS-CA), pp. 23 - 30, Santorini Island, Greece, June 11, 2008.
224) Wei Zhao* and Yao Liang, A Systematic Probabilistic Approach for Estimation in Dense Wireless Sensor Networks, Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 3285 - 3290, Las Vegas, USA, March 31- April 3, 2008.
225) Fei Huang* and Yao Liang, A Generic Analytical Model of Packet Combining in Wireless Sensor Networks, Proc. IEEE Wireless Communications and Networking Conference (WCNC), pp. 2555 - 2560, Las Vegas, USA, March 31- April 3, 2008.
226) Fei Huang* and Yao Liang, Towards Energy Optimization in Environmental Wireless Sensor Networks for Lossless and Reliable Data Gathering, IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS), 6 pages, Pisa, Italy, Oct., 2007.
227) Nimmy Ravindran* and Yao Liang, HIDE – A Web-based Hydrological Integrated Data Environment, Proc. 8th International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), Vol. 3, pp. 143 – 148, Qingdao, China, July 30 – Aug. 1, 2007.
228) Mei Han* and Yao Liang, Virtual Queue Based RED-VBR, System and
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Information Sciences Notes (now renamed as Communications of SIWN), Vol. 1, No. 3, pp. 251 – 254, July 2007. (Also in Proc. 2007 SIWN International Conference on Complex Open Distributed Systems, 2007).
Editorials
1) Arjan Durresi, Guest Editor for the IEEE Communication Magazine , Special Issue on Networking Issues for Cloud Computing published in September 2012
2) Arjan Durresi, Guest Editor for the IEEE Communication Magazine, Special Issue on Future Internet Architectures: Design and Deployment Perspectives, published in July 2011.
Conference Presentations
1 Hernandez, F., L. Li, *S. Gangaraju, X. Liang, Y. Liang, and W. Teng, Hydrologic severity-
based forecast system for road infrastructure monitoring, American Geophysical Union Fall
meeting, San Francisco, CA, December 9-13, 2013.
2 Villalba, G., X. Liang, D. Salas, and Y. Liang, Using graphical models to infer missing
streamflow data with its application to the Ohio river basin, American Geophysical Union Fall
meeting, San Francisco, CA, December 9-13, 2013.
3 Liang, X., D. Salas, *M. Navarro, Y. Liang, W. Teng, R. Hooper, P. Restrepo, and J. Bales,
(invited), A new open data open modeling framework for the geosciences community,
American Geophysical Union Fall meeting, San Francisco, CA, December 9-13, 2013.
4 *M. Navarro, T. W. Davis, Y. Liang, and X. Liang, ASWP: A Long-Term WSN Deployment
for Environmental Monitoring, The 12th ACM/IEEE Conference on Information Processing in
Sensor Networks (IPSN), pp. 351-352, Philadelphia, Pennsylvania, April 8–11, 2013, poster
presentation.
5 X. Liang, T. Davis, T. Hare, *M. Navarro, and Y. Liang, An Experimental Study of a WSN
System for Environmental Monitoring, American Geophysical Union Fall meeting, San
Francisco, CA, December 5-9, 2011.
6 T. W. Davis, C.-M. Kuo, H. van Hemmen, E. Ferrris, A. Aouni, Y. Liang, and X. Liang,
"Wireless Sensor Network Field Study: Network Development, Installation, and
Measurement Results," American Geophysical Union (AGU) Fall meeting, San Francisco,
CA, 2010, poster presentation.
7 D. Salas, X. Liang, and Y. Liang, A Systematic Approach for Hydrological Model Couplings,
2nd IEEE International Conference on Cloud Computing Technology and Science,
Indianapolis, IN, Nov. 30-Dec. 3, 2010, poster presentation.
8 Z. Nan, S. Wang, X. Liang, T. Adams, W. Teng, and Y. Liang, A Novel Hydro-information
System for Improving National Weather Service River Forecast System, American
Geophysical Union (AGU) Fall Meeting, San Francisco, CA, December 14-18, 2009, poster
presentation.
9 T. Davis, C. Kuo, Yao Liang, and X. Liang, Application of wireless sensor networks for
environmental monitoring, American Geophysical Union 2009 Joint Assembly – The Meeting
of the Americas, Toronto, Canada, May 24-27, 2009, poster presentation. (Outstanding
Student Paper Award)
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10 Zhuotong Nan, Shugong Wang, Xu Liang, Thomas Adams, William Teng, and Yao Liang,
Analysis of spatial similarities between Stage III NEXRAD precipitation and LDAS combo
precipitation data products, American Geophysical Union 2009 Joint Assembly – The
Meeting of the Americas, Toronto, Canada, May 24-27, 2009, oral presentation.
11 X. Liang, S. Wang, Z. Nan, T. Adams, W. Teng, L. Chiu, and Yao Liang, Fusing precipitation
for NOAA’s AWIPS DSS through a hydro-information, American Geophysical Union Fall
Meeting, San Francisco, CA, December 15–19, 2008, poster presentation.
12 Yao Liang, T. Adams, X. Liang, W. Teng, and L. Chiu, A Novel Hydro-information System for
Improving NOAA’s AWIPS DSS for Disaster Management, Earth Science Information
Partners (ESIP) Federation Summer Meeting, Madison, WI, July 17-20, 2007, poster
presentation.
13 Yao Liang, T. Adams, X. Liang, W. Teng, and L. Chiu, A Novel Hydro-information System for
Improving NOAA’s AWIPS DSS for Disaster Management, American Geophysical Union Fall
meeting, San Francisco, CA, December 10–14, 2007, poster presentation.
Abstracts and Posters
1) Arjan Durresi, “Integrated Health Coach System.” IUPUI Research Day, Indianapolis, IN, April 13, 2012.
2) Arjan Durresi, “Secure and Economically Viable Internet Architecture.” IUPUI Research Day, Indianapolis, IN, April 8, 2011.
3) Arjan Durresi, “Architecture for the Future Internet.” IUPUI Research Day, Indianapolis, IN, April 9, 2010.
Grants
External
1) National Science Foundation (NSF# 1249678): "An Application Delivery Platform for Mobile
Apps on Global Clouds", (PI: Arjan Durresi), ($ 60,000): NSF, 2012-2014.
2) National Science Foundation (NSF# 1019120): "Large-Scale Distributed Scientific
Experiments on Shared Substrates", (PI: Arjan Durresi), ($ 100,000): NSF, 2010-2015.
Research Experiences for Undergraduates ($16,000).
3) National Science Foundation (NSF# 1138659): "Using Lessons from the Disaster in Japan
to Develop Communications for Emergency Situations", (PI: Arjan Durresi), ($ 25,000): NSF,
2011-2013.
4) Microsoft Research Project Hawaii, (PI: Arjan Durresi), ($ 8,000 in kind): NSF, 2011-2013.
5) NSF #1262984, REU Site: Enhancing Undergraduate Experience in Mobile Computing
Security, 6/1/2013–5/31/2016, $360,000, Shiaofen Fang, Mohammad Al Hasan, X. Zou
(with Feng Li from CIT as PI).
6) Northrop Grumman, MovingCloud: Create Moving-target Defense in Cloud by Learning from
Botnets, 10/1/2012–9/31/2013, $107,000, X. Zou (with Feng Li from CIT as PI)
7) CISCO, Building A Secure Video Streaming Framework for Dynamic and Anonymous
Subscriber Groups, 07/15/08 – 07/15/09. $85,000, X. Zou.
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8) NSF, Secure Group Communications over Wired/Wireless Networks, (CCR-0311577),
08/01/03 - 07/31/07, $349,990, X. Zou (PI at IUPUI) (with B. Ramamurthy from UNL as
leading PI and V. Variyam from UNL as Co-PI).
9) Department of Veterans Affairs, Secure and Reliable Medical Information Systems, 01/01/06
- 12/31/07, $156,000 (initially awarded), X. Zou (with Y. Dai. as Co-PI)
10) NSF (CNS-1320132) Collaborative Research: Compressed Network Tomography and Data
Collection in Large-Scale Wireless Sensor Networking, (PI: Yao Liang), $233,108,
10/1/2013-9/30/2016.
11) Department of Transportation (DOT), Improving Hydrologic Disaster Forecasting and
Response for Transportation by Assimilating and Fusing NASA and Other Data Sets, (IUPUI
PI: Yao Liang), $131,463, 2/1/2014-1/31/2016
12) NSF (CNS-1252066) EAGER: Collaborative Research: Network Inference and Data
Collection Based on Compressed Sensing in Large-Scale Wireless Sensor Networking, (PI:
Yao Liang), $64,105; 9/15/2012-8/31/2014.
13) NSF (EAR-1245171) EAGER: Collaborative Research: From Data to Users: A Prototype
Open Modeling Framework, (PI: Yao Liang), $88,495; 7/15/2012-6/30/2014.
14) National Aeronautics and Space Administration (NASA), Improving Pennsylvania
Department of Transportation Hydrologic Disaster Forecasting and Response by
Assimilating and Fusing NASA and Other Data Sets, (IUPUI PI: Yao Liang), $61,467;
9/1/2012-12/31/2013.
15) NSF (NSF# CNS-0721474) Collaborative Research: Investigating Temporal Correlation for
Energy Efficient and Lossless Communication in Wireless Sensor Networks, (PI: Yao Liang),
$220,879; 9/1/2007-8/31/2012.
16) NASA, (NASA# NNA07CN83A) Enhancing NOAA AWIPS DSS by Infusing NASA Research
Results for Drought and Other Disaster Management, (PI: Yao Liang), $601,481; 11/1/2007
– 11/15/2011.
Internal
1) Purdue University Summer Research Grant, Research and Evaluation of Privacy-Preserving
and Replaceable Biometrics-based Authentication, 2011, $8,000, X. Zou.
2) Indiana University’s Center for Applied Cybersecurity Research (CACR) Grant,
Evaluation of Clinical and Genomic Information Privacy Risks from Inference Attacks,
06/01/10–07/31/11, $49,952, X. Zou (and with J. Chen from SoI as PI)
3) Indiana University’s Center for Applied Cybersecurity Research (CACR) Grant, A novel
approach to resilent, secure, and cancellable biometrics, 07/01/09–07/31/10, $33,736,, X.
Zou with S. Orr (and E. Y. Du originally from ECE as PI).
4) Center for Applied Cybersecurity Research: “Secure Communications among Cell Phones
and Sensors for Medical Applications”, (PI: Arjan Durresi), ($ 50,000): CACR, 2010-2012.
5) Purdue University, “Secure & Economically Viable Support for Internet Mobility”, (PI: Arjan
Durresi), ($ 17,000): 2010-2012.
6) IUPUI IDF, “Security and Mobility Architecture for the Next Generation Internet”, (PI: Arjan
Durresi), ($ 15,000): 2009-2011.
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7) Purdue University Summer Research Grant, Implementation and Evaluation of Secure,
Compos- able, and Scalable Framework for Trusted Collaborative Computing, 2008, $8,000,
X. Zou
8) Signature Center for Bio-Computing, IUPUI, 2007–2009, $285,000, X. Zou with S. Fang as
PI and others.
Educational Research Group
Referred Journal Publications, Books, & Book Chapters
HTML / XHTML / CSS All in One for Dummies Wiley Press 2008 (A Harris, C McCullough)
PHP6 / MySQL Programming for the Absolute Beginner Course Technology 2009 (A Harris)
JavaScript and AJAX for Dummies Wiley Press 2010 (A Harris)
HTML / XHTML / CSS All in One for Dummies 2nd Ed Wiley Press 2011 (A Harris)
HTML5 Quick Reference for Dummies Wiley Press 2012 (A Harris)
HTML5 Game Programming for Dummies (2013) Refereed Conference and Workshop Publications
Scratch@MIT – Massachusetts Institute of Technology 2012 – Adding a Game
Programming Component to National Science Olympics using Scratch A Harris
Grants
Computer Science Pilot Grant, 2013-2014, awarded by the AP College Board, July 2013
(Michele Roberts)
Development and Implementation of Web-based Modules for a Diabetes education Program in the Pediatric Outpatient Setting, 2014 (Stancombe, K, Andrew Harris IU Health Values Fund for Education $100,000 2014-2015) Curriculum Enhancement Grant, May 2013 (Kathy Marrs, Senehasis Mukhopadhyay, Michele
Roberts)
Curriculum Enhancement Grant, IUPUI Center of Teaching and Learning, $7,600, May 2013
(Lingma Acheson)
Integrative Department Grant, awarded by the IUPUI Center of Teaching and Learning, 2009
(Michele Roberts)
Curriculum Enhancement Grant, awarded by the IUPUI Center of Teaching and Learning, 2008
(Michele Roberts)
ICHE Teacher Training Partnership Grant, 2003 (Nguyen, K., Kastberg, S., Michele Roberts)
Curriculum Vitae
Lingma L. Acheson Department of Computer and Information Science, IUPUI
Email: [email protected] | Phone: (317)2749733
Education
MS in Computer Science, IUPUI 2004
BS in English Education, Suzhou University, China, 1989
Teaching Experience
2007 Fall - Present: Lecturer, Computer Science Department, IUPUI
2007 spring: Part-time Instructor, Computer Science Department, IUPUI
2004 – 2007: Adjunct Faculty in Chinese Language, Butler University,
Indianapolis, IN
1999 – 2000: Adjunct Faculty in English, San Jiang University, Jiangsu, China
1997 – 1998: Visitor Scholar, Poudre School District, Fort Collins, Colorado, USA
1989 – 1999: English Teacher, Changzhou Tourism School, Jiangsu, China
Non-Academic Experience
2004 – 2007: Database Administrator and Webmaster, School of Engineering and
Technology, IUPUI
1995 – 1997: Associate Director of Curriculum Department, Changzhou Tourism
School, Jiangsu, China
1994 – 1995: Associate Director of Principal’s Office, Changzhou Tourism School,
Jiangsu, China
1989 – 1994: Director of Student Organizations and Activities. Changzhou Tourism
School, Jiangsu, China
Honors and Awards
Recipient of the IUPUI 2009 Glenn W. Irwin, Jr. M.D. Experience Excellence
Recognition Award
First Prize in Changzhou Teacher’s Qualification Contest, 1997
First Prize in Changzhou English Speech-Making Contest, 1997
Second Prize Instructor’s Award in National Makeup Art Contest of Tourism Schools,
1997
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Third Prize Instructor’s Award in National Makeup Art Contest of Tourism Schools,
1997
First Prize Instructor’s Award in Makeup Art Contest of Jiangsu Tourism Schools, 1995
First Prize Instructor’s Award in Spoken English Contest of Jiangsu Province, 1995
Best Essay Award in Fifth Annual National Beauty Art Seminar of China, 1994
First Prize in Changzhou Teaching Essays Contest, 1993
First Prize in Changzhou Teaching Skills Contest, 1992
First Prize in Changzhou Teaching Planning Contest, 1992
Third Prize in Changzhou Teaching Skills Contest, 1989
University Academic Excellence Award (top 5%), 1986, 1987, 1988
Publications
Acheson, D., Acheson, L.: Implementing a Database Drive Solution for Nominations and
Elections of Faculty Governance. ASEE 2007
Mahoui, M., Lu, L., Gao, N., Li, N., Chen, J., Bukhres, O., Ben-Miled, Z.,: A Dynamic
Workflow Approach for the Integration of Bioinformatics Services. Cluster Computing
8(4): 279-291 (2005)
Ben-Miled, Z., Lu, L., Mahoui, M., Chen, J., Bukhres, O., Gao, N., He, Y.: A Service
Discovery Approach in Support of Web Service Integration. BIBE 2004: 65-72
Ben-Miled, Z., Gao, N., Bukhres, O., Lu, L., Li, N., He, Y., Mahoui, M., Chen, J.:
SIBIOS: A System for the Integration of Bioinformatics Services. CLADE 2004: 74
Lu, L.: On Training Students to Be Professional Beauty Artists. Fifth National
Conference of Association for Beauty Art, Beijing, China, January 1994
Lu, L.: Innovative Ways of Presenting Course Materials. 1993 Seminar on English
Teaching, Changzhou, Jiangsu, China, December 1993
Lu, L.: Effectively Memorizing English Words. 1992 Conference of Secondary School
English Teaching, Changzhou, Jiangsu, China, April, 1992
Lu, L.: Avoiding Interference of Mother Tongue in Foreign Language Teaching. 1989
Seminar on English Teaching, Changzhou, Jiangsu, China, December 1989
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NAME Jake Y. Chen, Ph.D.
EDUCATION
2001 Ph.D. The University of Minnesota, Twin Cities, MN Department of Computer Science and Engineering Thesis: A Bioinformatics Discovery-oriented Computing Framework
1997 M.S. The University of Minnesota, Twin Cities, MN Department of Computer Science and Engineering Minor in Biochemistry, Biophysics, and Molecular Biology
1995 B.S. Peking University, Beijing, China Department of Biochemistry & Molecular Biology, College of Life Sciences GRE Biochemistry Subject Test (1995): 99%
ACADEMIC APPOINTMENTS
2012—present Visiting Professor, Zhejiang Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical College, China
2010— present Associate Professor of Informatics (with tenure), Indiana University School of Informatics,
Indianapolis, IN
2010— present Associate Professor of Computer Science (joint appointment), Department of Computer and Information Science, Purdue University, Indianapolis, IN
2007— present Founding Director, Indiana Center for Systems Biology and Personalized Medicine, Indiana
University – Purdue University, Indianapolis, IN
2004— 2010 Assistant Professor of Informatics, Indiana University School of Informatics, Indianapolis, IN
2004— 2010 Assistant Professor of Computer Science (joint appointment), Department of Computer and Information Science, Purdue University, Indianapolis, IN
INDUSTRIAL & ENTREPRENEURIAL EXPERIENCE
2011— present Founding Board Director, Health and Science Innovations, Inc., Indianapolis, IN
2006— present Founder and Chairman, Medeolinx, LLC., Indianapolis, IN
PROFESSIONAL AFFILIATIONS
Senior Member, Association for Computing Machinery (ACM) 2009— present
Chair, IEEE Engineering in Medicine & Biology Society (EMBS), Central Indiana Chapter 2005— present Proteomics Chair, the Life Science Society (LSS) 2005— 2008 Senior
Member, Institute of Electrical and Electronic Engineers (IEEE) 2004— present Co-
founder /Steering Committee Member, IN Biomedical Entrepreneur Network (IBEN) 2004— 2009
Board Member, Association of Chinese Bioinformaticians (ACBIX) 2001— 2007
Member, International Society of Computational Biology (ISCB) 1996— present
HONORS
2012 Innocentive Grand Challenge Award Winner
2011 Inaugural IUPUI School of Informatics Research Award
2011 “17 Informatics Experts Worth Listening To”, HealthTechTopia 2009 IUPUI Translational Research into Practice (TRIP) Scholar Recognition Award 2008
IUPUI Chancellor’s Prestigious External Awards Recognition (PEAR)
2008 & 2007 Indiana Small Business Grant Preparation Award (for MedeoLinx, LLC, $12K)
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RESEARCH GRANT AWARDS
Note: Current pending proposals are not listed.
[1] PI: “Zhejiang Institute of Biopharmaceutical Informatics and Technologies—a collaboration between IUPUI and
Wenzhou Medical College”. 12/1/2012–11/30/2015, 2M Chinese Yuan (~$330K total), Wenzhou Medical College, China.
[2] IU Subcontract PI: “R21: A Systems Biology Modeling of Functional and Precursor Myeloid- derived Suppressor Cells”. $350K (my direct cost is $150K total), 9/1/2012–08/31/2014, National Institute of Health (PI: Timothy Ratliff, Purdue University).
[3] IU Subcontract PI: “R01: Initiation and Regulation of Chronic Autoimmune Prostate Inflammation”. ~$1M (my direct cost is ~$100K total), 4/1/2012 –03/31/2017, National Institute of Health (PI: Timothy Ratliff, Purdue University).
RESEARCH PUBLICATIONS
* Indicates serving as a corresponding/co-corresponding author.
[1] Huajun Chen*, Tong Yu*, and Jake Y. Chen* (2013) Semantic Web Meets Integrative Biology: A Survey. Briefings in Bioinformatics, Vol. 14, No. 1, pp. 109-125. (ISI journal impact factor = 5.20)
[2] Liang-Chin Huang, Xiaogang Wu, and Jake Y. Chen* (2012) Predicting Adverse Drug Reactions by Integrating Protein Interaction Networks with Drug Structures. Proteomics, Vol. 13, No. 2, pp. 313-324. (ISI journal impact factor = 4.51)
[3] Jake Y. Chen, Mohammed Zaki, Mohammad Hasan, and Jun Huan (2011) Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics. Published by ACM SIGKDD.
[4] Vincent S. Tseng, Hui-Huang Hsu, and Jake Y. Chen (2010) Proceedings of the Second International
Workshop on Data Mining in Bioinformatics. Published by IEEE press.
[5] Jake Y. Chen and Stefano Lonardi, ed. (2009) Biological Data Mining. 656 pages. Published by Chapman & Hall/CRC, USA. ISBN: 978-1420086843.
SOFTWARE AND PATENTS
[1] PAGED: Pathway And Gene-set Enrichment Database (2012) Developed at Indiana University. http://bio.informatics.iupui.edu/PAGED/ with Hui Huang and Xiaogang Wu.
[2] HOMER: Human Organ-specific Molecular Electronic Repository (2011) Developed at Indiana University. http://bio.informatics.iupui.edu/HOMER/ with Fan Zhang.
PROFESSIONAL SERVICE External Grant Review Panels Special Emphasis Panel, AREA, Pennsylvania Department of Health, Genome Canada, National Institute of Health, Emerging Technologies and Training in Neurosciences (Neurotechnology Study Section), American Institute of Biological Sciences / Department of Defense, Department of Energy, National Science Foundation Journal Editorial Board Personalized Medicine, Network Biology, International Journal of Functional Informatics and Personalized Medicine, BMC Systems Biology (as Associate Editor), Proteomics Insights
UNIVERSITY SERVICE
Campus Committees
IUPUI Graduate Fellowship Committee, IUPUI Research Support Fund Grant (RSFG) Review Committee, IUPUI Program Review and Assessment (PRAC) Committee
School Committees
IUPUI School of Informatics Budgetary Affairs Committee Chair, IUPUI School of Informatics Faculty Search Committee, IUPUI School of Informatics Colloquium Committee, IUPUI School of Informatics Nomination Committee
Department Committees
Bioinformatics MS/PhD Program Curriculum Committee, Bioinformatics MS/PhD Program Graduate Admissions Committee, IUPUI School of Informatics Bioinformatics Faculty Search Committee
173
Raymond C. Y. Chin
Department of Mathematical Science Indiana University Purdue University
Indianapolis
402 N. Blackford Street Indianapolis, IN 46202-3216 (317) 274-6998 (O)
Education
1970 Ph.D. Case Western Reserve University, Cleveland, OH 1964
M.A.E. Rensselaer Polytechnic Institute, Troy, NY
1962 B.A.E. Rensselaer Polytechnic Institute, Troy, NY
Professional Experience
1/05 - Professor Department of Mathematical Sciences,
Indiana University Purdue University Indianapolis
7/97 - 12/05 Professor Department of Computer & Information Science, Indiana University Purdue University Indianapolis
7/90 - 6/97 Professor & Chair Department of Computer & Information Science, Indiana University Purdue University Indianapolis
Professional service
6/95 - 6/02 Ad-hoc Member NIH study section, SSS-9
6/02 - 6/07 Ad-hoc member NIH Bio Computing and Bio-informatics Study Section
9/89 - 9/00 Reviewer NSF proposals and Panels
3/95 - present Reviewer Louisiana’s Board of Regents
Editorship
• 1976-1990 Editor, Journal of Computational Physics
Teaching Grants
1. NSFs Instrument and Laboratory Improvement Grant, 1993; Chin, R. C. Y., Ng, B. S., Cox, R. W., and Palakal,M. J., “Computing Science and Mathematics: A Laboratory-Based Curriculum”
2. Indiana University Strategic Direction Implementation Grant, 1995; Springer, G. and Chin, R. C. Y., ”A System-Wide, Modular, Non-Major Curriculum in Computing”
3. NSF Course and Curriculum Grant, DUE #9729433, 1997; Chin, R. C. Y. and Springer, G., “Miracle: AnEnvironment for Teaching Programming”
4. IUPUI Honors Program Research Fellow 2000/2001
5. IUPUI Honors Program Research Fellow 2001/2002
6. IUPUI Multidisciplinary Undergraduate Research Institute grant, 2007
7. IUPUI Multidisciplinary Undergraduate Research Institute grant, 2008
8. IUPUI Multidisciplinary Undergraduate Research Institute grant, 2009
Selected Publications
174
1. R. C. Y. Chin, and D. Ridzal, “Generating Orthogonal Polynomials for Exponential Weights on a FiniteInterval,” Proceedings of International Workshop on Special Functions, C. Dunkl, M. Ismail, &R. Wong, eds, World Scientific, Singapore, 2000, ISBN-981-02-4393-6.
2. D. R. Reitman, Z. Ben-Miled, J. Schild, and R. C. Y. Chin; Synthesis of ionic currents using reconfig- urablehardware”, IEEE Electro/Informatoion Technology Conference, June 8-11, 2000.
3. Madden, J. L, Z. Ben-Miled, J. Schild, and R. C. Y. Chin; ”On parameter estimation for neuron models,”IEEE International Symposium on Bio-Informatics and Biomedical Engineering.
4. Z. Ben-Miled, D. R. Reitman, J. Schild, and R. C. Y. Chin; ”On the design of reconfigurable hardware tools forinvestigating ion channel dynamics”, ISCA Journal of Computers and Their Applications, 9/2000
5. L. Lang, M-G, Meng, R. C. Y. Chin, A. Lucksiri, D., Flockhart, and S. Hall; “Drug-Drug Interaction Prediction: ABayesian Meta-Analysis Approach,” Statistics in Medicine, accepted for publication
Selected Presentations
1. R. C. Y. Chin, “Generating Orthogonal Polynomials for Exponential Weights on a Finite Interval,” International
Workshop on Special Functions - Asymptotics, Harmonic Analysis and Mathematical Physics, Hong Kong, June21-25, 1999.
2. D. Ridzal* and R. C. Y. Chin, “Polyalgorithm Design and Testing - Generating Orthogonal Polynomials forExponential Weights on a Finite Interval,” Eleventh Annual Argonne Symposium for Undergrad- uates in Science,Engineering and Mathematics, November 3-4, 2000, Argonne National Laboratory, Argonne, IL.
3. B. Bieth, and R. C. Y. Chin, “Gaussian Quadrature for an Exponential Weight and Stiff ODEs,” SIAM NationalMeeting, New Orleans, LA, July 11-15, 2005.
4. B. Bieth, R. C. Y. Chin, B. Muhoberac, and V. Vavinskiy*, “A Re-examination of the Quasi-Steady- StateApproximations for Reversible Enzyme Kinetics,” SIAM National Meeting, New Orleans, LA, July 11-15, 2005.
5. B. Bieth, R. C. Y. Chin, and Lang, L., “A Drug-Drug Interaction Parameter Estimation Problem,” SIAMNational Meeting, Boston, MA, July 10-14, 2006.
Courses Taught
• CSCI 207 Data Analysis Using a Spreadsheets
• CSCI 340 Discrete Computational Structures
• CSCI 414 Numerical Methods
• CSCI 475 Scientific Computing I
• CSCI 476 Scientific Computing II
• CSCI 512 Numerical Method s for Engineers and Scientists
• CSCI 514 Numerical Analysis
• CSCI 515 Numerical Analysis of Linear Systems
• CSCI 520 Computational Methods in Analysis
• CSCI 590 Modeling and Computation in Neuroscience
• CSCI 590 Mathematical and Computational Physiology
• CSCI 614 Numerical Solution of Ordinary Differential Equations
• Math 163 Integrated Calculus and Analytic Geometry I
• Math 261 Multivariate Calculus
• Math 426 Introduction to Applied Mathematics and Modeling
• Math 559 Applied Computational Methods I
• Math 552 Applied Computational Methods II
• Math 511 Linear Algebra with Applications
• Math 578 Mathematical Modeling of Physical Systems, I
• Math 692 Topics in Applied Mathematics
• Math 692 Topics in Applied Mathematics: Mathematical Physiology
175
M. Murat Dundar, Ph.D. Assistant Professor Department of Computer & Information Science IUPUI, Indianapolis, IN 46202 Email: [email protected] Phone: (317)278-6488
a. Professional Preparation
Bogazici University, Istanbul Electrical & Electronics Engineering BSc 1997
Purdue University, West Lafayette Electrical & Computer Engineering MS 1999
Purdue University, West Lafayette Electrical & Computer Engineering PhD 2003
b. Appointments
2008-Present Assistant Professor, Department of Computer & Information Science, Indiana University – Purdue University, Indianapolis 2003-2008 Research Scientist, Computer-aided Diagnosis & Knowledge Solutions, Siemens Healthcare, USA.
c. General Summary
My area of expertise is in machine learning and data mining with a more recent focus on non-parametric Bayesian models and inference, learning with partially-observed data, online and offline class discovery and modeling. My research is mainly driven by real-world problems in computer aided diagnosis/detection, hyper-spectral data analysis and remote sensing, bio-detection, flow cytometry data analysis, information retrieval, and topic modeling. I am a co-author on over 30 peer-reviewed publications and a co-inventor in 5 patents and 2 FDA-approved computer-aided diagnosis products. I have served as a PC member for ACM SIGKDD, IEEE ICDM, SIAM SDM conferences and as a panelist for NIH and NSF review panels. I and my colleagues at Siemens Health received the Data Mining Practice Prize Award for our work on medical image mining by ACM SIGKDD in 2009. I am the main author of the paper that received the best scientific paper award in the Bioinformatics and Biomedical Applications track at the 20th International Conference on Pattern Recognition (ICPR'10). I have most recently received the 2013 NSF Early Faculty Career Development (CAREER) Award.
d. Publications (* indicates students advised by the PI)
1. Murat Dundar, Halid Ziya Yerebakan, Bartek Rajwa, "Batch Discovery of Recurring RareClasses toward Identifying Anomalous Samples," To appear at SIGKDD 2014. (acceptance rate:15%)
2. James C Costello et al., "A community effort to assess and improve drug sensitivity predictionalgorithms," Nature Biotechnology, June 2014
3. Murat Dundar, Bartek Rajwa, Lin Li, “Partially-observed Models for Classifying Minerals onMars,” In Proceedings of WHISPERS'13, Gainesville, FL, June 25-28, 2013.
4. Ferit Akova, Yuan Qi, Bartek Rajwa, Murat Dundar, “Self-adjusting Models for Semi-supervisedLearning in Partially-observed Settings,” In Proceedings of the IEEE International Conference onData Mining (ICDM’12), Brussels, Belgium, December 10-13, 2012. (acceptance rate: 11%)
5. Murat Dundar, Ferit Akova, Yuan Qi, Bartek Rajwa, “Bayesian Nonexhaustive Learning forOnline Discovery and Modeling of Emerging Classes,” In John Langford and Joelle Pineau (Eds.),Proceedings of the 29th International Conference on Machine Learning (ICML'12), Edinburgh,Scotland, June 26-July 1, 2012 (pp. 113-120). Omnipress, 2012.
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6. Murat Dundar, Sunil Badve, Gokhan Bilgin, Vikas Raykar, Olcay Sertel, Metin N. Gurcan,“Computerized Classification of Intraductal Breast Lesions using Histopathological Images”, IEEETransactions on Biomedical Engineering, 58(7):1977-1984, 2011.
7. Ferit Akova*, Murat Dundar, V. Jo Davisson, E. Daniel Hirleman, Arun K. Bhunia, J. Paul
Robinson, Bartek Rajwa, “A Machine-learning Approach for Label-free Detection of UnmatchedBacterial Serovars”, Statistical Analysis and Data Mining Journal, Volume 3, No 5, pp 289-301,October 2010.
8. Murat Dundar, Sunil Badve, Vikas Raykar, Rohit Jain, Olcay Sertel, Metin Gurcan, “A MultipleInstance Learning Approach toward Optimal Classification of Pathology Slides”, Proc. of 20
th
International Conference on Pattern Recognition (ICPR’10), August 23-26, Istanbul, Turkey, 2010(Best scientific paper in Biomedical and Bioinformatics applications).
e. Synergistic Activities
Co-inventor on 10 patent applications and key machine learning scientist in two FDA-approved computer-aided diagnostic systems currently deployed in hundreds of hospitalsaround the globe.
Organizing committee member KDD’10, PC member KDD’12, ICDM’12, SDM’13, SDM’!4,ICDM’14.
NSF IIS Panel Reviewer 2012 and 2013, NIH BMIT-B Panel Reviewer 2012
Founding board member of the Truebright Science Academy, Web:http://www.truebright.org(Grades 6-12, over 90% African-American) (2006)
Governing board member for the Indiana Math and Science Academy (IMSA)Web:http://www.imsaindy.org (K-12). Two locations in Indianapolis with over 900 students(over 80% African American) (2008-present)
f. Collaborators and Other Affiliations:
Collaborators and Co-Editors: Sunil Badve (IUPUI, Associate Editor, Clinical Breast Cancer), Arun K. Bhunia (Purdue University), Jinbo Bi (University of Connecticut), Glenn Fung (Siemens Healthcare), Metin N. Gurcan (The Ohio State University, Associate Editor, IEEE TMI), E. Daniel Hirleman (UC Merced), Balaji Krishnapuram (Siemens Healthcare), Yuan Qi (Purdue University), Bartek Rajwa (Purdue University, Associate Editor Cytometry Part A), R. Bharat Rao (Siemens Healthcare, General Chair KDD’10), Vikas Raykar (Siemens Healthcare), J. Paul Robinson (Purdue University)
Graduate Advisors and Postdoctoral Sponsors David Landgrebe (Purdue University)
Thesis Advisor and Postgraduate Scholar Sponsor Ferit Akova, Gokhan Bilgin, Allison Irvine, Halidziya Yerebakan
g. Recent Grants:
1. Self-adjusting Models as a New Direction in Machine Learning (funded by NSF CAREER)Duration: 3/13-2/18
2. Automated Spectral Data Transformations and Analysis Pipeline for High Throughput FlowCytometry (funded by NIH/NIBIB)Duration: 7/12-6/14Role: co-investigator
3. Machine-learning Approach to Label-free Detection of new Bacterial Pathogens (funded byNIH/NIAID)Duration: 5/10-4/12Role: PI
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Arjan Durresi Professor
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-8942
Web: http://www.cs.iupui.edu/~durresi
E-mail: [email protected]
a. Professional Preparation
Polytechnic University of Tirana, Electronic Engineering B. E. 1986
Polytechnic University of Tirana, Electronic-Telecommunication Engineering M. S. 1990
Italian Telecommunication Institute, Superior Specialization in Telecommunication 1991
Polytechnic University of Tirana, Telecommunication Engineering Ph.D. 1993
Tokyo Denki University, Tokyo, Japan, Computer Science Ph.D. 2006
b. Appointments
Professor, Department of Computer and Information Science, IUPUI 2013 – Present
Associate Professor, Department of Computer and Information Science, IUPUI 2007-2013
Assistant Professor, Department of Computer Science, Louisiana State University 2003 – 2007
Research Scientist, Department of Computer and Information Science, The Ohio State University 1996 – 2003
Associate Professor, Chairman of the Telecommunication Dep., Polytechnic University of Tirana 1994 – 1996
Senior Software Analyst and Designer, R&D Department at Telesoft Inc., Rome, Italy 1991 – 1994
c. General Summary
My research focuses on networking, security and trust. I am particularly interested in new network architectures as
response to the changing challenges and needs of users in various environments and applications, such as Internet,
wireless, optical, multimedia, and so on. Important design goals for such systems include scalability, security,
robustness, reliability, economic viability, manageability. My research explores the design space among various
goals and constrains and tries to find desirable tradeoffs, which would enable the practical use of new solutions.
Furthermore, I work to develop trust management systems, by using measurement theory to evaluate trust. I am the
co-author of over seventy five papers in journals and over 170 papers in conference proceedings, seven book
chapters, and over thirty contributions to standardization organizations such as IETF, ATM Forum, ITU, ANSI and
TIA. Several of my papers has received conference awards. I have been keynote speaker in conferences including
IEEE AINA and NBiS. I am the PI of four NSF funded research projects. My research has also been funded by the
States of Ohio and Louisiana, as well as university and industry sources.
d. Recent Publications
1. Ping Zhang, Arjan Durresi, Leonard Barolli, “Policy-based mobility in heterogeneous networks,” J. Ambient
Intelligence and Humanized Computing, Vol. 4, No. 3, pp. 331-338, 2013
2. Ping Zhang, Arjan Durresi, Raj Jain, “Cloud aided Internet Mobility,” in Proceedings IEEE International
Conference on Communications ICC2013, Budapest Hungary, June 9-13, 2013, pp. 3688-3693
3. Harold Owens II, Arjan Durresi, Raj Jain, “Video over Software-Defined Networking (VSDN),” in Proceedings
of 16th International Conference on Network-Based Information Systems- NBiS 2013, 2013, pp. 44-51
4. Ping Zhang, Arjan Durresi, Raj Jain, “Economically Viable Support for Internet Mobility,” in Proceedings
IEEE International Conference on Communications ICC2011, Kyoto, Japan, June 5-9, 2011
178
5. Arjan Durresi, Ping Zhang, Mimoza Durresi, Leonard Barolli, “Architecture for Mobile Heterogeneous Multi
Domain Networks,” International Journal of Mobile Information Systems, Vol. 6, No. 1, 2010, pp. 49-63
6. Arjan Durresi, Mimoza Durresi, Leonard Barolli, “Network Trust Management in Emergency Situations,”
Journal of Computer and System Sciences, Vol. 77, No. 4, 2011, pp. 677-686
7. Arjan Durresi, Vijay Bulusu, Vamsi Paruchuri, Leonard Barolli “Secure Emergency Communication of Cellular
Phones in Ad Hoc Mode,” Ad Hoc Networks Journal, Vol. 5, No. 1, 2007, pp. 126 – 133
8. Arjan Durresi, Vamsi Paruchuri, “Broadcast Protocol for Energy-Constrained Networks,” IEEE Transaction on
Broadcasting, Vol. 53, No. 1, 2007, pp. 112-119
9. Arjan Durresi, Vamsi Parachuri, S. Iyengar, and Rajgopal Kannan, “Optimized Broadcast Protocol for Sensor
Networks”, IEEE Transaction on Computers, Vol. 54, No. 8, August, 2005, pp. 1013 – 1024
10. Arjan Durresi, Vamsi Paruchuri, “Secure Communication among Cell Phones and Sensor Networks,” in
Proceedings of IEEE GLOBECOM 2009, Honolulu, Hawaii, USA, November 30 - December 4, 2009, pp. 167–
174
e. Synergistic ActivitiesProgram vice Chair for Security and Trustworthy Computing 17
th IEEE International Conference on Parallel and
Distributed Systems - IEEE ICPADS2011, Chair of 13th
International Conference on Network-Based Information
Systems - NBiS2009; Chair of the 23rd
IEEE International Conference on Advanced Information Networking and
Applications - AINA2009; PC Chair of the Fourth International Conference on Availability, Reliability and Security
ARES2009; PC Co-Chair of AINA2006; Program Area Chair for "Security in Ad hoc and Sensor Networks" at AINA
2005. Founder of the IEEE Workshops on Heterogeneous Wireless Networks HWISE and Co-Chair in 2005-14;
Founder the International Workshop on Advances in Information Security - WAIS2007 and Co-Chair in 2007-14;
and Founder of the IEEE Workshop on BioComputing and Co-Chair in 2008-14; Founder of the International
Workshop on Trustworthy Computing TwC and Co-Chair in 2014. Associate Editor of the International Journal of
Virtual Technology and Multimedia IJVTM, Interscience Publishers; Area Editor of Ad Hoc Networks Journal,
Elsevier; Journal of Network and Computer Applications, Elsevier; Transactions on Networks and Communications,
ICST; and Editorial Board Member of Journal of Ubiquitous Computing and Intelligence - JUCI, American
Scientific Publishers. “Patterns in Network Architecture,” Keynote Speech at NBiS2008, Turin, Italy, September 1-
5, 2008; “Designing the Future Internet,” Keynote Speech at IEEE AINA2007, Niagara Falls, Canada May 21-23,
2007.
g. Collaborators & Other AffiliatesCollaborators and Co-Editors: Dr. Raj Jain (WUSTL), Dr. Makoto Takizawa (Seikei University, Japan), Dr.
Leonard Barolli (Fukuoka Institute of Technology, Japan), Dr. Akio Koyama (Yamagata University, Japan), Dr. M.
Uehara (Toyo University, Japan), Dr. Tomoya Enokido (Rissho University, Japan), Dr. Fatos Xhafa (Universitat
Politècnica de Catalunya Barcelona, Spain).
Thesis Advisor and Postgraduate-Scholar Sponsor: Ph. D.: Ping Zheng (IUPUI), Yefeng Ruan (IUPUI), Harold
Owens (IUPUI), Lina Alfantoukh (IUPUI), Vamsi Paruchuri (UCA).
h. Recent Grants
PI, An Application Delivery Platform for Mobile Apps on Global Clouds, NSF, 2012-2014
PI, RAPID: Using Lessons from the Disaster in Japan to Develop Communications for Emergency Situations,
NSF, 2011-2013.
PI, Large-Scale Distributed Scientific Experiments on Shared Substrate, NSF Computer and Network
Systems, 2010-14.
PI, Secure Communications among Cell Phones and Sensors for Medical Applications, Center for Applied
Cybersecurity Research - CACR, 2010-2011.
179
Shiaofen Fang
723 W. Michigan St., SL 280 Ph: 317-274-9731 Indianapolis, IN 46202 E-mail: [email protected]
EDUCATION:
Ph.D, Computer Science, University of Utah, 1992.
M.S., Applied Mathematics, Zhejiang University, China, 1988.
B.S. Mathematics, Zhejiang University, China, 1983.
APPOINTMENTS:
July 2007 – Present: Chair, Department of Computer and Information Science, Indiana University Purdue University Indianapolis (IUPUI)
September 2006 -- June 2007 : Interim Chair, Department of Computer and Information Science, IUPUI
July 2009 – Present : Professor, Department of Computer and Information Science, IUPUI
July 2009 – June 2011, School of Science Director of Information Technology, Indiana University Purdue University Indianapolis (IUPUI)
August 2002 – June 2009: Associate Professor, Department of Computer and Information Science, IUPUI
August 1996 – June 2002 : Assistant Professor, Department of Computer and Information Science, IUPUI
December 1993 – August 1996 : Research Staff, Center for Information Enhanced Medicine (CiMed), National University of Singapore and The Johns Hopkins University.
September 1992 – December 1993 : Assistant Professor, CAD program, School of Architecture, The Ohio State University.
July 1988 -- June 1992 : Graduate Research and Teaching Assistant, Department of Computer Science, University of Utah
July 1986 -- June 1988 : Research Staff, CAD/CAM Center, Zhejiang University, China
PROFESSIONAL SERVICES AND RECOGNITIONS:
Panelists, National Science Foundation (NSF), Review Panels on Career Award Panel (Media and Informatics), HCI, ITR, IIS, etc.
Panelist, Chinese National Science Foundation (CNSF), Special Oversea Invitation.
International Proposal Reviewer for: Hong Kong Research Grants Council; Singapore National Research Foundation; Singapore Science and Engineering Research Council; Qatar National Research Fund, etc.
Program Committee Members / Program Chair: ACM VRST, CGI, ACM/SIGGRAPH VRCAI, CAD/Graphics International Conference, ACM SAC, Workshop on Bio-Computing (Program Co-Chair), etc.
Keynote Speaker, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, June, 2011.
1999 & 2004: Trustee’s Teaching Awards, School of Science, IUPUI.
PATENTS
Shiaofen Fang and Hongsheng Chen, Method and Apparatus for Fast Voxelization of Volumetric Models, US patent, US6556199.
Jake Chen, and Shiaofen Fang. 4-protein biomarker panel for the diagnosis of lymphoma from biospecimen. WO 2011097476 A1, US 20130058863 A1.
MAJOR EXTERNAL RESEARCH GRANTS
Health-Terrain: Visualizing Large Scale Health Data, PI, Department of the Army – USAMRAA, ERMS# 12108017, $661,035, 3/1/2013 – 8/31/2014.
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REU Site: Enhancing Undergraduate Experience in Mobile Computing Security, co-PI (PI: Feng Li), National Science Foundation, $359,964, 6/1/2013 – 5/31/2016
3D Facial Imaging on FASD , co-PI (15% and 1 graduate student support; PI: Tatiana Foroud), National Institutes of Health (NIH), $1,500,000, 06/01/08 – 05/31/13.
Mouse Model Neuro-Facial Dysmorphology: Translational and Treatment Studies, co-PI (10%, and 1 graduate student support; PI: Feng Zhou), National Institutes of Health (NIH), $1,200,000, 06/01/08 – 05/31/13.
A Cross-Cultural Longitudinal Assessment of FASD, Co-PI (15%, and 1 graduate student support; PI: Tatiana Foroud), National Institutes of Health (NIH), $662,733, 9/29/03 – 9/28/07.
Deformable Volume Modeling, National Science Foundation (NSF), $120,171, 7/15/98 – 12/31/01, role: PI.
SELECTED RECENT PEER REVIEWED PUBLICATIONS (From a total of 75)
1. Jing Wan, Zhilin Zhang, Bhaskar Rao, Shiaofen Fang, JIngwen Yan, Andrew Saykin, Li Shen. Identifyingthe Neuroanatomical Basis of Cognitive Impairment in Alzheimer’s Disease by Correlation- andNonlinearity-Aware Sparse Bayesian Learning. IEEE Trans. On Medical Imaging. Accepted.
2. Qian You, Shiaofen Fang and Patricia Ebright, Iterative Visual Clustering for Learning Concepts fromUnstructured Text Unstructured Text Data. International Journal of Software and Informatics, Volume 6,Issue 1 (2012), pp. 43-59.
3. Yishi Guo, Yang Wang, Shiaofen Fang, Hongyang Chao, Andrew J. Saykin1, Li Shen. Pattern Visualizationof Human Connectome Data. Eurographics Conference on Visualization (EuroVis) (2012) M. Meyer and T.Weinkauf (Editors). Pp 78-83.
4. Jing Wan, Zhilin Zhang, Jingwen Yan, Taiyong Li, Bhaskar Rao, Shiaofen Fang, Sungeun Kim, ShannonRisacher, Andrew Saykin, Li Shen. Sparse Bayesian Multi-Task Learning for Predicting CognitiveOutcomes from Neuroimaging Measures in Alzheimer's Disease. CVPR2012: IEEE InternationalConference on Computer Vision and Pattern Recognition, June 18-20, 2012.
5. Li T, Wan J, Zhang Z, Yan J, Kim S, Risacher SL, Fang S, Beg MF, Wang L, Saykin AJ, Shen L, for theADNI (2012) Hippocampus as a predictor of cognitive performance: Comparative evaluation of analyticalmethods and morphometric measures. MICCAI 2012 Workshop on Novel Imaging Biomarkers forAlzheimer's Disease and Related Disorders, Nice, France, October 5, 2012
6. Jason Mclaughlin, Shiaofen Fang, Sandra Jacobson, H Eugene Hoyme, Luther Robinson and TatianaForoud. Interactive Feature Visualization and Detection for 3D Face Classification, International Journal ofCognitive Informatics and Natural Intelligence. 5(2), 2011, 1-16.
7. Luoding Zhu, Guowei He, Shizhao Wang, Laura Miller, Xing Zhang, Ray Chin, Qian You, Shiaofen Fang,An immersed boundary method by the lattice Boltzmann approach in three dimensions, Computers &Mathematics with Applications (CMA, by Elsevier), 61(12), pp 3506–3518.
8. Qian You, Shiaofen Fang, Jake Chen, “GeneTerrain: Visual Exploration of Differential Gene ExpressionProfiles Organized in Native Biomolecular Interaction Networks”, Journal of Information Visualization, 2010;9:1, 1-12.
9. Jianbing Shen, Hanqiu Sun, Jiaya Jia, Hanli Zhao, Xiaogang Jin, Shiaofen fang. A unified framework fordesigning textures using energy optimization, Pattern Recognition, 43:2, pp. 457-469, 2010.
10. Jianbing Shen, Shiaofen Fang, Hanli Zhao, Xiaogang Jin. Fast Approximation of Trilateral Filter for ToneMapping Using a Signal Processing Approach. International Journal of Signal Processing, Elsevier, 2009,89:5. 901-907.
11. Shiaofen Fang, Basil George and Mathew Palakal. Automatic Surface Scanning of 3D Artifacts,International Journal of Virtual Reality. 2009, 8:4, 67-72.
12. Shiaofen Fang, Jason McLaughlin, Jiandong Fang, Jeffrey Huang, Ilona Autti-Rämö, Åse Fagerlund, et al.,“Automated Diagnosis of Fetal Alcohol Syndrome Using 3D Facial Image Analysis”, Orthodontics andCraniofacial Research, 2008;11:162-171.
13. Jie Chen, Shuning Li and Shiaofen fang, “Quantification of Tooth Displacement from Cone Beam CTImages”, American Journal of Orthodontics & Dentofacial Orthopedics, 136:3, pp:393-400,2009.
181
John M. Gersting, Jr. Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-9727 E-mail: [email protected]
a. Professional PreparationPh.D. Engineering Science, Arizona State University, 1970 M.S. Engineering Science, Arizona State University, 1964 B.S. Engineering Science, Purdue University, 1962
b. Appointments2009 – present Professor Emeritus, Department of Computer and Information Science, IUPUI 2009 – present Professor Emeritus, Department of Mechanical Engineering, IUPUI
c. General Summary1991-2009 Professor of Computer Science and Engineering
University of Hawaii at Hilo Hilo, HI
1981-1991 Professor of Computer and Information Science Indiana University-Purdue University at Indianapolis
1981-1984 Staff Scientist Indianapolis Center for Advanced Research
1980-1981 Associate Professor of Computer Science University of Central Florida Orlando, FL
1970-1979 Assistant, Associate, Professor of Computer Science and Engineering Science Indiana University-Purdue University at Indianapolis
2013 Outstanding Associate Faculty Award Purdue School of Engineering and Technology Indiana University-Purdue University at Indianapolis
Research areas: database and numerical methods Developed numerous textbook manuals for service courses at the University of Hawaii at Hilo
d. Recent PublicationsGersting J.M. and Rothe, C.F., "Cardiovascular Interactions Tutorial: Architecture And Design", J. Med Systems, vol 26, No. 1 (2002), 29-38
Rothe, C.F., and Gersting, J. M., "Cardiovascular Interactions: An Interactive Tutorial and Mathematical Model", Adv. Physiol. Educ., vol 26, No. 2 (2002), 98-109
e. Synergistic Activities1975-2013 Software developer for Department of Medical and Molecular Genetics,
Indiana University School of Medicine
2009 Board Member, IndyPASS (Indianapolis Professional Association for SQL Server)
2005-2007 Instructor, NSF Chautauqua short course on "Using Access, SQL Server, SQL, and XML in Your Database Course", University of Dayton
1972-present Member of ACM (Association for Computing Machinery)
g. Collaborators & Other Affiliates
h. Recent Grants
182
Judith Gersting Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-9727 E-mail: [email protected]
a. Professional PreparationPh.D. Mathematics, Arizona State University, 1969 M.S. Mathematics, Arizona State University, 1964 B.S. Mathematics, Stetson University, 1962
b. Appointments2009 – present Professor Emeritus, Department of Computer and Information Science, IUPUI
c. General Summary1990-2009 Professor of Computer Science
University of Hawaii at Hilo Hilo, HI (UHH) Department Chair 1994-2009
1981-1990 Professor of Computer and Information Science Indiana University-Purdue University at Indianapolis Acting Department Chair 1981-1982
1982-1984 Staff Scientist Indianapolis Center for Advanced Research
1980-1981 Associate Professor of Computer Science University of Central Florida Orlando, FL
1970-1979 Assistant, Associate, Professor of Computer and Information Science Indiana University-Purdue University at Indianapolis
Research areas: Computer science education, fault-tolerant computing
d. Recent PublicationsGersting, J. L., Mathematical Structures for Computer Science, Seventh Edition, W. H. Freeman and Company, 2014 (969 pages)
Gersting, J. L., and Givan, Robert, Instructor's Resource Manual for Mathematical Structures for Computer Science, Seventh Edition, W. H. Freeman and Company, 2014 (470 pages).
Schneider, G. M. and Gersting, J. L., Invitation to Computer Science, 6th Edition, Cengage Learning, 2013 (855 pages)
Schneider, G. M. and Gersting, J. L., Invitation to Computer Science, 5th Edition, Course Technology, 2010 (718 pages)
Edwards, H. K., Gersting, J. L., and Tangaro, T., “Teaching Alice in Hawai'i: Cultural Perspectives”, Proc. of the Frontiers in Education Conference, Milwaukee, WI, October 10-13, 2007. IEEE & Stipes Publishing, LLC. ISBN 1-4244-1084-3. p.T3A1 – T3A5.
Gersting, J. L., Mathematical Structures for Computer Science, Sixth Edition, W. H. Freeman and Company, 2007 (807 pages).
183
Gersting, J. L., Instructor's Manual for Mathematical Structures for Computer Science, Sixth Edition, W. H. Freeman and Company, 2007 (331 pages).
Schneider, G. M. and Gersting, J. L., Invitation to Computer Science, C++ Version, 4th Edition, Course Technology, 2007 (719 pages)
Schneider, G. M. and Gersting, J. L., Invitation to Computer Science, Java Version, 3rd Edition, Course Technology, 2007 (720 pages)
Erdogan S. S., Gersting J. L., Shaneyfelt, T. and Duke, E., "Using FPGA Technology Towards the Design of an Adaptive Fault Tolerant Framework", Proc. of the IEEE International Conference on Systems, Man and Cybernetics 2005, Vol. 4. pp. 3823- 3827. Waikoloa, HI, October 10-12, 2005
e. Synergistic Activities2011, Reviewer for Discrete Structures knowledge area for the ACM/IEEE CS2013 curriculum guidelines
2000-present, Referee for IEEE Transactions in Education
1997 – 2001, 2003- 2013, Referee for SIGCSE Technical Symposium on Computer Science Education
2008, 2009 NSF Reviewer for CCLI grant program, Washington DC
2007, DHS Reviewer for Scientific Leadership Awards, Washington DC Sept. 10-13
2007, Poster Session on Alice Programming, 38th SIGCSE Technical Symposium on Computer Science Education, Covington, KY
2007, Instructor, NSF-sponsored short course " Programming with Alice: A New Strategy for Introductory Computer Science Courses", University of Dayton, Dayton, OH
2007, Attended Google Faculty Summit for Computer Science, Mountain View CA
2007, NSF Reviewer for Foundation Pathways to Revitalized Undergraduate Computing Education (CPATH) program, Washington D.C.
2006, Attended NSF-sponsored Stanford by-invitation workshop on “Integrative Computing Education and Research: Preparing IT Graduates for 2010 and Beyond”, Stanford University,
1987-2009, Member of Editorial Board, Computer Science Education
1981-present Member of ACM (Association for Computing Machinery) and SIGCSE
g. Collaborators & Other AffiliatesG. Michael Schneider, Macalester College
h. Recent Grants2005-2008, National Science Foundation Grant ($59,262), "Alice in Paradise: Adapting 3-D graphics interactive animation for teaching an introductory computer science (CS-0) course in Hawaii"
184
Andrew Harris
Sr. Lecturer, Department of Computer and Information Science
Indiana University / Purdue University – Indianapolis
1. Curriculum Vitae
a. Education
MS Informatics - New Media IUPUI 2003
BS Special Education, IUPUI 1990
b. Academic Appointments
2006 – Present: Sr. Lecturer, Department of Computer and Information Science Indiana University / Purdue
University – Indianapolis
2010 – Present: Instructor – In-Grace Homeschool cooperative High School Computer Science and Algebra
(Volunteer)
1998 – 2006: Lecturer, Department of Computer and Information Science Indiana University / Purdue University
– Indianapolis
1995 – 1998: Visiting Lecturer, Department of Computer and Information Science Indiana University / Purdue
University - Indianapolis
c. Non-Academic Experience
2011 – Present: Columnist The Old Schoolhouse Magazine “The Tech Homeschooler”
2000 – Present: Author, Premiere Press Fast and Easy Series, Absolute Beginner Series
2000 – Present: Editor (series editor Absolute Beginner Series) and technical editor - Wiley Press, Premiere Press
2005 – Present: Author, Wiley Press, Dummies Series, L-Line Series
2003 – Present: Consultant, Indiana Vocational Rehabilitation Services
d. Honors and Awards
Teaching Excellence Recognition Award (TERA), IUPUI School of Science, 1997
Outstanding Associate Faculty, IUPUI School of Science, 1998
Teaching Excellence Recognition Award, IUPUI School of Science, 1999
Who's Who Among American Teachers, Student Nomination, 2003
School of Science Trustee’s Teaching Award, 2004
2. Grants
Curriculum Improvement Grant, School of Science, IUPUI. Worked on use of streaming media, television,
virtual reality and traditional media resources to offer high-quality course offerings
Cultural Literacy Indexing Our Heritage, Assisted with streaming media production and inclusion.
Using Mobile Games to Teach Children to manage Juvenile Diabetes (ongoing with Riley Children's Hospital.)
3. Publicationsa. Books
JavaScript Programming for the Absolute Beginner (Prima Tech, 2001)
Palm Programming for the Absolute Beginner” (Premier Press, 2001)
C# Programming for the Absolute Beginner (Premier Press, 2002)
PHP/ MySQL Programming for the Absolute Beginner (Premier Press, 2003)
PHP 5 /MySQL Programming for the Absolute Beginner (Premier Press, 2004)
Beginning Flash Game Programming for Dummies (Wiley Press, 2005)
Game Programming – The L Learning Line (Wiley Press, 2006)
HTML / XHTML / CSS All in One for Dummies (Wiley Press, 2008)
PHP 6 / MySQL Programming for the Absolute Beginner (Premier Press, 2009)
JavaScript & AJAX for Dummies (Wiley Press. 2010)185
HTML / XHTML / CSS All in One for Dummies 2nd
Ed (Wiley Press, 2011)
HTML5 Quick Reference for Dummies (Wiley Press, 2012)
HTML5 Game Programming for Dummies (Wiley Press 2013)
HTML5 / CSS3 All in one For Dummies (Wiley Press 2014)
b. Magazine Articles
Teach yourself Game Programming with Scratch (Feature story: The Old Schoolhouse Magazine, May 2012)
The Tech Homeschooler – Monthly column on the use of computing, coding, and technology for those who
choose to teach partially or completely at home.
c. Book Chapters
Text Editing with Emacs - Published in several Linux books for Prima Tech, and released as a standalone
publication in 2002
4. Conference Presentations
HTML5 is the new foundation of the Web – Indy Drupal Camp, September 2011
Build your Own Video Game! Adding Computing to your Curriculum Midwest Homeschool Convention
Cincinnati OH May 2012
5. Selected Software Releases
SIMILE – A custom XML language with parser and editor for building synchronized instructional materials from
browser history. (GPL)
Miracle – A tool for teaching algorithmic programming. Students enter an algorithm and it is converted to one of
a number of programming langauges. (GPL)
Abniac – A very simple 7-opcode assembly language simulator. Can be used to teach even children how an
assembly language works. (GPL)
dbLib – A PHP module that automates building a 3-tier application by introspection of a MySQL database (GPL)
gameLib.py – A Python game engine providing another layer on top of pyGame / SDL. This library encapsulates
the main look and provides a much improved sprite object as well as basic GUI capabilities. (GPL)
SimpleGame.js – A JavaScript / HTML5 library that vastly simplifies the creation of web and mobile games with
the HTML5 canvas. (GPL)
6. Selected Course Development
CSCI 23000 Computing I – This was the traditional Computing I course offered by the CS department since its
inception. Goal of course redesign was to make course more attractive to a wide range of students will still
retaining focus on learning algorithm development and essential computer science skills. Currently taught in
Python.
CSCI 24000 Computing II – This advanced programming course is intended for experienced programmers ready
to transition to a study of formal computer science. The course uses a historical view of programming languages
using multiple languages (C, C++, and Java) to illustrate and practice concepts including pointers, compound
variables, stack and heap, object-oriented programming, makeFiles, exception-handling, and object serialization.
CSCI N341 Client-side Web Development – This course has gone through several iterations. Current version
focuses on client-side web development in JavaScript and jQuery.
CSCI N342 Server-side Web Development – Illustrates the development of multi-tiered web applications with a
focus on server-side development in PHP. Strong database management focus including data normalization and
implementation in MySQL.
CSCI N351 Multimedia Development – Intended for majors and non-majors. This course begins with digital
representation of analog signals, and describes how Nyquist's law is used in multiple forms of media. Students
study audio, raster images, vector images, 3D scenegraphs, procedural and U/V textures, animation, and video. In
addition, various forms of compression and file formats are explored for each media type. All exercises are done
with open-source software tools.
CSCI N451 Web Game Development – Teaches 2D game development with multiple tools. Begins with a pre-
built HTML5 game engine for quick success. Students then dig into Python / PyGame for more detailed study of
the game development process. Students finish the class with a web, mobile, or console game of their own design.
186
Mohammad Al Hasan Assistant Professor
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-3862
Web: http://www.cs.iupui.edu/~alhasan
E-mail: [email protected]
a. Professional PreparationInstitute Degree Date awarded
Rensselaer Polytechnic Institute, Troy, NY PhD, Computer Science August 2009
University of Minnesota, Twin Cities, MN MS, Computer Science August 2002
Bangladesh University of Engineering and
Technology, Dhaka, Bangladesh
BSc (Engg.), Computer Science and
Engineering
July 1998
b. AppointmentsInstitution Role Inclusive Dates
Indiana University Purdue University Indianapolis,
IN
Assistant Professor,
Computer and
Information Science
August 2010 – Present
eBay Inc, San Jose, CA Senior Research
Scientist
July 2009 – August 2010
Rensselaer Polytechnic Institute, Troy, NY Teaching / Research
Assistant, CS Dept.
August 2004 – June 2009
University of Minnesota, Twin Cities, Minneapolis,
MN
Teaching / Research
Assistant, CS Dept.
September 1999 –
December 2002
Ahsanullah University of Science and Technology,
Dhaka, Bangladesh
Lecturer, Computer
Science
August 1998 – September
1999
d. Recent Publications
1. Mansurul Bhuiyan, and ▲,‡Mohammad Al Hasan (2014). : FSM-H:Frequent
Subgraph Mining Algorithm in Hadoop. In Proc. of IEEE International Congress on
Big Data. Anchorage, Alaska, USA
2. Mahmudur Rahman, Mansurul Bhuiyan, and ▲,‡,†Mohammad Al Hasan (2014).
GRAFT: An Efficient Graphlet Counting Method for Large Graph Analysis. IEEE
Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2013.2297929
3. Mahmudur Rahman, Mansurul Bhuiyan, Mahmuda Rahman, ▲,‡,†Mohammad Al
Hasan (2013). GUISE: A Uniform Sampler for Constructing Frequency Histogram of
Graphlets. Knowledge and Information Systems Journal, 38 (3), 511-534
4. ▲,† Mohammad A. Hasan, Mohammed J. Zaki (2011). A Survey of Link Prediction in
Social Networks, in Chapter 9, Social Network Data Analytics, editor: Charu
Aggarwal, Springer
187
5. ▲,†Mohammad Al Hasan, Nish Parikh, Gyanit Singh, and Neel Sundaresan (2011).
Query Suggeston for E-commerce Sites, In Proc. of fourth ACM International
Conference on Web Search and Data Mining, Hong Kong, pages:765-774
6. ▲,†Mohammad Al Hasan, Hilmi Yildirim and Abhirup Chakrabarty (2010). SONNET:
Efficient Approximate Nearest Neighbor using Multi-Core, Proc. of 10th IEEE
International Conference on Data Mining (ICDM), Sydney, Australia, pages: 719-724
7. †Mohammad Al Hasan, and Mohammed J. Zaki (2009). Output Space Sampling for
Graph Patterns, in Proc. of VLDB Endowment, Lyon, France, 2 (1), pages: 730-741
8. †Mohammad Al Hasan, W. Scott Spangler, Thomas Griffin, and Alfredo Alba (2009).
COA: Finding Novel Patents through Text Analysis, in Proc. of 15th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining, Paris, France,
pages: 1175-1184
9. †Mohammad Al Hasan and Mohammed J. Zaki (2009). MUSK: Uniform Sampling of
k Maximal Patterns, in Proceedings of 9th SIAM International Conference on Data
Mining, Sparks, NV, 2009, pages: 650-661
10. Zujun Shentu, Mohammad Al Hasan, Chris Bystroff, and Mohammed J. Zaki (2008):
Context shapes: Efficient complementary shape matching for protein-protein docking,
in PROTEINS: Structure, Function, and Bioinformatics, 70 (3), 1056-1073
e. Synergistic Activities
Organizing committee member SDM’14, PC member ICDM’14, CIKM’14,BIGData’14, SDM’14, ICDM’14.
Journal article reviewer IEEE TKDE, IEEE TNNLS, DMKD, ACM TWEB, KAIS(Springer), IJAIT, VLDB, JMLR, PLOS One, ACM Survey
Tutorial Speaker, KDD’13, ICDM’13
NSF IIS Panel Reviewer Fall 2010, Spring 2012, NSF SBIR Fall 2013; GrantProposal reviewer, Lousiana Board of Regents in Fall 2013 and NetherlandOrganization for Scientific Research in Spring 2013,
Indiana State Science Fair Judge, 2012 - 2014
g. Collaborators & Other Affiliates
Member: IEEE, SIAM, ACM Mohammad Hasan’s Doctoral Advisor: Mohammed J Zaki Doctoral Students: Mahmudur Rahman, Mansurul Bhuiyan, Tanay Saha, Baichuan Zhang, and Vachik Dave
h. Recent Grants
NSF CAREER: A novel framework for mining graph patterns in large biological and social
networks (Principal Investigator), 547-427, March 2012 – February 2017
188
James H. Hill, M.S., Ph.D. Assistant Professor
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-8527
Web: http://www.cs.iupui.edu/~hillj
E-mail: [email protected]
a. Professional Preparation
2009 Ph.D. in Computer Science, Vanderbilt University, Nashville, TN
2006 M.S. in Computer Science, Vanderbilt University, Nashville, TN
2004 B.S. in Computer Science, Morehouse College, Atlanta, GA
b. Appointments
6/2013 – 5/2014 Advisory Board, Department of Continuing Education, IUPUI
8/2009 – present Assistant Professor of Computer Science, IUPUI
c. General Summary
Dr. Hill’s research focuses on evaluating performance of software systems during early phases of
the software lifecycle. His interests are in model-driven engineering, software system emulation,
software system instrumentation, software performance analytics, and its application towards
understanding performance properties of large-scale software system early in the software
development lifecycle. Dr. Hill has published more than 55 peer-reviewed research papers since
2007 (more than 40 in rank while at IUPUI). The applied nature of his research has results in
research artifacts currently being used in both academia and industry.
d. Recent Publications
1. Gunter Mussbacher, Daniel Amyot, Ruth Breu, Jean-Michel Bruel, Betty Cheng, Philippe
Collet, Benoit Combemale, Robert France, Rogardt Heldal, James Hill, Jörg Kienzle,
Matthias Schöttle, Friedrich Steimann, Dave Stikkolorum, Jon Whittle. (2014, September).
Model-driven Engineering: Thirty Years From Now? ACM/IEEE 17th International
Conference on Model Driven Engineering Languages and Systems, Valencia, Spain
2. Manjula Peiris, James H. Hill, Jorgen Thelin, Sergey Bykov, Gabriel Kliot, and Christian
Konig (2014, June). PAD: Performance Anomaly Detection in Multi-Server Distributed
Systems. 7th IEEE International Conference on Cloud Computing, Alaska, USA.
3. Peiris, M., & Hill, J. H. (2013). Adapting system execution traces to support analysis of
software system performance properties. Journal of Systems and Software, 86(11), 2849-
2862
4. Dennis Feiock and James H. Hill (2013, September). Using Component-based Middleware to
Design and Implement Data Distribution Service (DDS) Systems. 39th Euromicro
Conference on Software Engineering and Advanced Applications, Santander, Spain.
5. Manjula Peiris, Mohammad Al Hasan, and James H. Hill (2013, June). Auto-Constructing
Dataflow Models from System Execution Traces. The 16th
IEEE International Symposium on
Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC), Paderborn,
Germany.
6. Dennis Feiock and James H. Hill (2013, June). Optimizing General-Purpose Software
Instrumentation Middleware Performance for Distributed Real-time and Embedded Systems,
189
The 16th
IEEE International Symposium on Object/Component/Service-Oriented Real-Time
Distributed Computing (ISORC), Paderborn, Germany.
7. Pati, T., & Hill, J. H. (2012). A survey report of enhancements to the visitor software design
pattern. Software: Practice and Experience.
8. Pati, T. and Hill, J. H. (2012, October). Proactive Modeling: Auto-Generating Models From
Their Semantics and Constraints. The 12th Workshop on Domain-Specific Modeling,
Tucson, AZ.
9. Hill, J.H. (2011, April). Measuring and Reducing Modeling Effort in Domain-specific
Modeling Languages with Examples. 18th IEEE International Conference and Workshops on
Engineering of Computer-Based Systems, Las Vegas, NV.
e. Synergistic Activities
Reviewer for IEEE Software, IEEE Computer, NSF Panels, and many conferences; Steering committee
for (and co-organizer of) Model-Driven Engineering for High Performance and Cloud workshop;
Chairmanship on the organizing committee of MODELS, OOPSLA, and many other conferences.
g. Collaborators & Other Affiliates
Dr. Mohammad Hasan (IUPUI); Dr. Rajeev Raje (IUPUI); Dr. Christian Rogers (IUPUI); Mr.
Clayton Nicholas (IUPUI); Mrs. Michelle Roberts (IUPUI); Dr. Bart Miller (University of
Wisconsin, Madison); Mr. Andy Harris (IUPUI); Dr. Gregory Klass (Georgetown University);
Dr. Eric Burger (Georgetown University); Mr. Kenneth Miller (University of Texas at Dallas);
Dr. Jorgen Thelin (Microsoft Research); Dr. Sergey Bykov (Microsoft Research); Dr. Gabriel
Kliot (Microsoft Research); Dr. Christian Konig (Microsoft Research); Dr. Jules White
(Vanderbilt University); Dr. Douglas Schmidt (Vanderbilt University)
h. Recent Grants
1. System Execution Modeling Environment Research and Development – Phase 5, Australia
Defense Science and Technology Organization (DSTO), 1/1/2014 - 12/31/2016, $120,000
USD.
2. Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phase 3. S2ERC (Sponsor:
Department of Homeland Security), 8/1/2013 – 5/31/2014, $10,000 USD
3. Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phase 2. S2ERC (Sponsor:
Department of Homeland Security), 8/1/2013 – 5/31/2014, $30,000 USD
4. System Execution Modeling Environment Research and Development – Phase 4, Australia
Defense Science and Technology Organization (DSTO), 1/1/2013 - 12/31/2013, $69,933
USD.
5. Testing-as-a-Service: Static Code Analysis (SCA) Tool Study – Phase 1. S2ERC (Sponsors:
Lockheed Martin & Northrup Grumman), 1/1/2013 – 12/31/2013, $49,060 USD.
6. Cyber-physical multi-core Optimization for Resource & cachE effectS (C2ORES), Office of
Naval Research, 7/3/2012 – 7/2/2012, $85,478 USD.
7. System Execution Modeling Environment Research and Development – Phase 3, Australia
Defense Science and Technology Organization (DSTO), 5/13/2012 - 5/13/2013, $105,000
USD.
8. Automatic Identification of Software Performance Anti-patterns in Cloud Computing
Applications, Amazon Inc., 1/1/2012 – 12/31/2013, $5,000.
190
Yao Liang
Professor
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202
Phone: 317-274-3473
Web: http://cs.iupui.edu/people/yao-liang
Email: [email protected]
a. Professional Preparation
1997 Ph.D. in Computer Science, Clemson University, Clemson, SC
1988 M.S. in Computer Science, Xi'an Jiaotong University, Xi'an, China
1982 B.S. in Computer Engineering, Xi'an Jiaotong University, Xi'an, China
b. Appointments
2013 – present Professor, Department of Computer and Information Science, Purdue School of Science,
Indiana University-Purdue University Indianapolis (IUPUI)
2007 – 2013 Associate Professor, Department of Computer and Information Science, IUPUI
2001 – 2007 Assistant Professor, Department of Electrical and Computer Engineering, Virginia Tech
1997 – 2001 Technical Staff Member, Alcatel USA, Raleigh, NC
c. General Summary
Dr. Yao Liang’s research focuses on the areas of wireless sensor networks, cyberinfrastructure, adaptive network
control and resource allocation, quality of service, machine learning, data mining, data fusion, hydro-informatics,
data management and integration, distributed systems, and nonlinear signal prediction. He has led an
interdisciplinary and multi-institutional NSF projects on developing sophisticated theoretical framework and
protocols for energy-efficient and reliable data collection, and dynamic network routing topology tomography in
environmental monitoring wireless sensor networks. He has also recently led an interdisciplinary and multi-
institutional NASA project and collaborating with hydrologists to innovatively infuse NASA’s newly available
remote sensing data and models into National Weather Service’s core operation to enhance its decision making
and weather forecasting performance for flooding and drought disaster management. He also has substantial
experiences and knowledge on building real-world testbed and prototype systems, such as building wireless
sensor network testbed for environmental monitoring (NSF projects) and the development of the Hydrological
Integrated Data Environment (HIDE) prototype system (NASA project). In addition, he has intensive research
and development experiences in telecommunications industry over his four years service as a technical staff
member at Alcatel, USA. He is a co-author of the work “Application of wireless sensor networks for
environmental monitoring” which has received the Outstanding Student Paper Award from American
Geophysical Union, 2009. He is a recipient of the University Trustees Teaching Award in 2011.
d. Recent Publications (* indicates student authors)
1. Yao Liang, Yimei Li*, An Efficient and Robust Data Compression Algorithm in Wireless Sensor Networks,
IEEE Communications Letters, Vol. 18, No. 3, pp. 439-442, March 2014.
2. M. Navarro*, T. W. Davis*, Y. Liang, and X. Liang, A Study of Long-Term WSN Deployment for
Environmental Monitoring, Proc. 24th IEEE International Symposium on Personal, Indoor and Mobile Radio
Communications (PIMRC), pp. 2098-2102, London, UK, Sept. 8-11, 2013.
3. Yao Liang, and Rui Liu*, Routing Topology Inference for Wireless Sensor Networks, ACM SIGACOMM
Computer Communication Review, Vol. 43, No. 2, pp. 22-27, April 2013.
4. T. Davis*, X. Liang, C.-M. Kuo*, and Y. Liang, Analysis of Power Characteristics for Sap Flow, Soil
Moisture and Soil Water Potential Sensors in Wireless Sensor Networking Systems, IEEE Sensors Journal,
Vol. 12, No. 6, pp. 1933-1945, 2012.
191
5. T. Davis*, X. Liang, M. Navarro*, D. Bhatnagar*, and Y. Liang, An Experimental Study of WSN Power
Efficiency: MICAz networks with XMesh, International Journal of Distributed Sensor Networks, Vol. 2012,
doi: 10.1155/2012/358238, 14 pages, 2012.
6. W. Zhao* and Y. Liang, Inference in Wireless Sensor Networks Based on Information Structure
Optimization, Proc. 37th IEEE Conference on Local Computer Networks (LCN), pp. 555-562, Clearwater,
USA, Oct. 22-25, 2012.
7. N. Erratt*, and Y. Liang, CDP: An Energy-Efficient Compressed Data-Stream Protocol for Wireless Sensor
Networks, IET Communications, Vol. 5, Iss. 18, pp.2673-2683, 2011.
8. M. Navarro*, D. Bhatnagar*, and Y. Liang, An Integrated Network and Data Management System for
Heterogeneous WSNs, The Eighth IEEE International Conference on Mobile Ad-Hoc and Sensor Systems
(MASS), pp. 819-824, Valencia, Spain, Oct. 17-22, 2011.
9. Y. Liang, and W. Peng*, Minimizing Energy Consumptions in Wireless Sensor Networks via Two-Modal
Transmission, Computer Communication Review, Vol. 40, No. 1, January 2010.
10. Zhuotong Nan, Shugong Wang*, Xu Liang, Thomas E. Adams, William Teng, and Yao Liang, Analysis of
spatial similarities between NEXRAD Stage III and LDAS Combo precipitation data products, IEEE Journal
of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 3, No. 3, pp. 371 – 385, 2010.
e. Synergistic Activities
Senior Member, IEEE
Editorial board member: International Journal of Distributed Sensor Networks (since 2014); The Open
Cybernetics and Systems Journal (since 2007)
Member, IEEE Signal Processing Society Machine Learning Technical Committee, 2004 – 2005
Member, Hydrological Information System of CUAHSI (Consortium of Univ. for the Adv. of Hydrologic
Science Inc.), 2003 – 2006
NSF proposal panelists
Invited talk speaker at various universities in USA, Europe and China
f. Collaborators and Other Affiliations
Collaborators: D. Maidment – UT Austin, J. Helly and I. Zaslavsky – San Diego Supercomputing Center; P.
Kumar – UIUC, M. Piasecki – Drexel Univ., T. E. Adams III – National Weather Service, W. Teng – NASA,
L. Chiu – George Mason Univ., X. Liang – Univ. of Pittsburgh, R. Hooper – CUAHSI, P. Restrepo – NWS,
J. Bales – USGS, S. Kempler – NASA, L. DaSilva, L. Mili, and A. Zaghloul – Virginia Tech, and M.
Zaghloul – George Wash. Univ.
Students supervised by Dr. Yao Liang: M. Navarro, W. Zhao, R. Liu, Y. Li, N. Erratt, X. Zhong, S.
Lochan, F. Huang, Q. Yu, W. Peng, S. Bhendigeri, M. Kishore, N. Ravindran, M. Han, D. Bhatnagar,
C. Nicholson, N. Vijayaraghavan, M. Balmakhtar, D. Wen, R. Wang, J. Hu
g. Recent Grants (funded)
1. Collaborative Research: Compressed Network Tomography and Data Collection in Large-Scale Wireless
Sensor Networking, Lead PI, NSF, Duration: 2013-2016
2. Improving Hydrologic Disaster Forecasting and Response for Transportation by Assimilating and Fusing
NASA and other Data Sets, subcontract PI, DOT, Duration: 2014-2016
3. EAGER: Collaborative Research: Network Inference and Data Collection Based on Compressed Sensing
in Large-Scale Wireless Sensor Networking, Lead PI, NSF, Duration:2012-2014
4. EAGER: Collaborative Research: From Data to Users: A Prototype Open Modeling Framework, PI,
NSF, Duration: 2012-2014
5. Improving Pennsylvania Department of Transportation Hydrologic Disaster Forecasting and Response
by Assimilating and Fusing NASA and Other Data Sets, subcontract PI, NASA, Duration: 2012-2013
192
Jing-Yuan Liu, Ph.D. Email: [email protected] Phone: 317-274-7645
EDUCATION AND TRAINING
Shandong University, China B.Sc. in Biochemistry 1995
Institute of Genetics, Chinese Academy of Sciences M.Sc. in Molecular Genetics 1998
Indiana University School of Medicine, Indianapolis IN Ph.D. in Structural Biology 2004
Indiana University School of Medicine, Indianapolis IN Postdoc in Computational Biology 2011
ACADEMIC POSITIONS
Indiana University Purdue University Indianapolis, Indianapolis, IN
Research Assistant Professor, Computer and Information Science 2011-present
Indiana University School of Medicine, Indianapolis, IN
Research Assistant Professor, Pharmacology and Toxicology 2011-present
HONORS
Annual Outstanding Student Award, Shandong University 1994
Outstanding Departmental Poster Presentation Award, IU School of Medicine 2002
Travel award, 10th SCBA International Meeting 2004
TEACHING ACTIVITIES
C572 Molecular Modeling. (Invited lecturer ) Dates: 2005
CSCI590 Algorithms for Bioinformatics. 14 lectures, 3 homework, 2 projects and 2 exams. Enrollment:
21 graduate students. Dates: 2012
CSCI590 Algorithms for Bioinformatics. 14 lectures, 3 homework, 2 projects and 2 exams. Enrollment:
27 graduate students. Dates: 2013
STUDENT SERVICE
Master students
Abhinav Kuru (M.Sc., 2011);
Divya Neelagiri (M.Sc., 2012);
Sonali Ranalkar (M.Sc., 2013);
Nilesh Ghadge (M.Sc., 2013);
Atchyutha Cherukuri (M.Sc., expected 2014);
Hema Kasi (M.Sc., expected 2014);
Sandeep Kumar (M.Sc., expected 2015);
Mahesh Yerram (M.Sc., expected 2015)
Ph.D. students
Valerie Fako (Ph.D. candidate, Co-mentor, expected 2015)
Postdoctoral fellow
Ravi Yadav (2012-2013)
PROFESSINAL SERVICE
Associate Editorial Board Member, Int. J. Biochem Mol Biol 2011-prst
Advisory Board Member, Current Cancer Drug Target 2013-prst
RESEARCH SUPPORT
Active:
193
Jing-Yuan Liu, Ph.D. Email: [email protected] Phone: 317-274-7645
DOD Prostate Cancer Research Program (PI: Zhang; Co-I: Liu) “Targeting survivin to overcome
acquired taxol resistance in prostate cancer chemotherapy” 2014-2017 Total: $576,000
Start-Up Fund (Liu) 2012-2015
The start-up fund provided by Indiana University School of Medicine and IUPUI School of Science is to
help Dr. Liu establish her laboratory and independent research career in computational biology and drug
discovery.
Pending
NIH R01 (PI) “In-vivo Chemical Probes Targeting Dimerization Core Unit of Survivin” Total direct:
$1,250,000.
NIH R01 (PI) “In vivo chemical probes targeting the ‘undruggable’ DNA binding site of STAT3” Total
direct: $1,250,000
Past
IUPUI iM2CS-GEIRE (Liu) “Analysis of protein-protein interactions by identification and investigation
of the dimerization cores” 2012-2013 Total: $15,000
ACS Institutional Award (Liu) “A novel approach targeting “undruggable” oncogenic protein dimers
for drug discovery” 2013-2014 Total: $40,000
PUBLICATIONS (*co-corresponding authors).1. Neher, TM.; Shuck, SC.; Liu, J.Y.; Zhang, JT.; Turchi, J.J. Identification of novel small molecule
inhibitors of the XPA protein using in silico based screening. ACS Chem. Biol. 15:953-965; 2010.
2. Liu, J.Y.; Hurley, TD. A new crystal form of mouse thiamin pyrophosphokinase. Int J Biochem Mol
Biol 2:111-118; 2011.
3. Mo, W.; Liu, J.Y.; Zhang, J.T. Biochemistry and pharmacology of human ABCC1/MRP1 and its role in
detoxification and in multidrug resistance of cancer chemotherapy. Recent Advances of Cancer
Research and Therapy (ed X.Y. Liu; S. Pestka; Y. Shi). Elsvier pp371-404; 2012
4. Liu, J.Y.*; Li, Z.; Li, H.; Zhang, J.T. A critical residue that promotes protein dimerization: a story of
partially exposed Phe25
in 14-3-3σ. J. Chem. Inf. Model. 51:2612-25; 2011.
5. Li, Z.; Peng, H.; Qin, L.; Qi, J.; Zuo, X.; Liu, J.Y.*; Zhang, J.T*. Determinants of 14-3-3σ dimerization
and function in drug resistance. J Biol Chem. 1;288(44):31447-57; 2013
6. Huang, W.; Dong, Z.; Wang, F.; Peng, H.; Liu, J.Y.*; and Zhang, J.T.* A small molecule inhibitor
targeting the “undruggable” DNA-binding site of human STAT3 inhibits cancer cell proliferation,
migration, and invasion. ACS Chem. Biol. 9:1188−1196; 2014.
7. Hu, G.; Liu, J.Y.*; and Wang, J.* Insight into conformational change for 14-3-3σ protein by
molecular dynamics simulation. Int. J. Mol. Sci. 15(2), 2794-2810; 2014.
8. Fako V.E.; Zhang, J.T.; and Liu, J.Y. Mechanism of Orlistat Hydrolysis by the Thioesterase of
Human Fatty Acid Synthase. ACS Catalysis (under revision)
9. Wang, C*; Kesi, H.*; and Liu, J.Y. Parallel vs anti-parallel conformation of nonphosphorylated
STAT1 (*authors contributed equally) (manuscript in preparation, expected to be submitted in June
2014)
10. Qi, J; Wang, C; Dong, Z; Liu, J; Liu, J.Y.*; Zhang, J.T.* A novel small molecule targeting the
dimeric interface of survivin (manuscript in preparation, expected to be submitted in June 2014)
11. Kuru, A.; Fang, S.; and Liu, J.Y. Principles and predictions of specific protein-protein interactions
(manuscript in preparation, expected to be submited in August 2014).
12. Yadav, RP; and Liu, J.Y. Distinct properties of conserved 14-3-3 human isoforms revealed by
molecular dynamic simulation and SAXS (manuscript in preparation, expected to be submited in
August 2014).
194
Dr. Snehasis Mukhopadhyay Department of Computer Science
Indiana University Purdue University Indianapolis 723 W. Michigan St., SL 280
Indianapolis, IN 46202 Phone: (317) 274-9732 Fax: (317) 274-9742
Email: [email protected]
Professional Preparation
B.E. in Electronics and Telecommunications, Jadavpur University, India, 1981 - 1985
M.E. in Systems Science and Automation, Indian Institute of Science, Bangalore, India, 1985 - 1987
M.S. in Electrical Engineering, Yale University, New Haven, CT, 1987 - 1991
Ph.D. in Electrical Engineering, Yale University, New Haven, CT, 1987 – 1994
Appointments
July 2010-Present: Professor, Computer & Information Science, IUPUI; co-director, Institute ofMathematical Modeling and Computational Science, IUPUI.
2001 – July 2010, Associate Professor, Computer & Information Science, IUPUI
2000 - 2006, Associate Director (Bioinformatics), School of Informatics, Indiana University
1995 - 2001, Assistant Professor, Computer & Information Science, IUPUI
1997 - 1997, Reader, Electronics & Telecommunications Engg, Jadavpur University
1994 - 1995, Visiting Assistant Professor, Computer & Information Science, IUPUI
1993 - 1994, Staff Fellow, Analytic Processes, GM NAO R&D Center, Warren, Michigan
1987 - 1993, Research and Teaching Assistants, Electrical Engineering, Yale University
Selected Recent Products
1. Singh, V. B., Mukhopadhyay, S., & Babbar-Sebens, M. (2013, October). User Modelling forInteractive Optimization Using Neural Network. In Systems, Man, and Cybernetics (SMC),2013 IEEE International Conference on (pp. 3288-3293). IEEE.
2. Tilak, O., Babbar-Sebens, M., & Mukhopadhyay, S. (2011, October). Decentralized andpartially decentralized reinforcement learning for designing a distributed wetland system inwatersheds. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conferenceon (pp. 271-276). IEEE.
3. Tilak, O., Martin, R., & Mukhopadhyay, S. (2011). Decentralized indirect methods for learningautomata games. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 41(5),1213-1223.
4. Tilak, O., & Mukhopadhyay, S. (2010, December). Decentralized and partially decentralizedreinforcement learning for distributed combinatorial optimization problems. In Machine
195
Learning and Applications (ICMLA), 2010 Ninth International Conference on (pp. 389-394). IEEE.
5. Babbar-Sebens, M., & Mukhopadhyay, S. (2009, October). Reinforcement learning forhuman-machine collaborative optimization: application in ground water monitoring. In Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on (pp. 3563-3568). IEEE.
Other Significant Products
1. Tilak, O., & Mukhopadhyay, S. (2011). Partially decentralized reinforcement learning in finite, multi-agent Markov decision processes. AI Communications, 24(4), 293-309.
2. Narendra, K. S., & Mukhopadhyay, S. (1997). Adaptive control using neural networks andapproximate models. Neural Networks, IEEE Transactions on, 8(3), 475-485.
3. Narendra, Kumpati S., and Snehasis Mukhopadhyay. "Associative learning in random environmentsusing neural networks." Neural Networks, IEEE Transactions on 2.1 (1991): 20-31.
4. Briceno, J. F., El-Mounayri, H., & Mukhopadhyay, S. (2002). Selecting an artificial neural networkfor efficient modeling and accurate simulation of the milling process. International Journal ofMachine Tools and Manufacture, 42(6), 663-674.
5. Narendra, K. S., Oleng, N., & Mukhopadhyay, S. (2006). Decentralised adaptive control with partialcommunication. IEE Proceedings-Control Theory and Applications, 153(5), 546-555.
Synergistic Activities
1. Editorial Board Member, Journal of Bioengineering & Biomedical Science, 20102. Proposal Reviewer and Invited Review Panelist for the National Science Foundation, 2004, 2009,
2011 3. Member of the Program Committee of IEEE Biocomputing Workshop, 2008, 2009.4. Member, IEEE Control Systems Society Technical Committee on Intelligent Control in charge of the
IEEE working group on Distributed-Information Control Systems, 1999– present.5. Invited participant and session leader for the break-out session on “Data Management and Mining” at
the NSF Workshop on Biomedical Informatics, Menucha, Oregon, 2007.
Recognitions and Honors
2014: Trustees’ Teaching Award, IUPUI 2005: Co-author, One of the Top Bioinformatics Papers, Biological Research Information Center Bioinformatics Online Newsletter, 2005 2000: The paper “Multi-agent Adaptive Dynamic Programming”, co-authored with my graduate student Joby Varghese, was selected as one of the ten best papers in the MICAI, 2000, International conference. 1996: NSF CAREER Award. 1996: Honored by the computer science club at IUPUI as the best professor for 300-500 level courses. 1995: The NET (Network for Excellence in Teaching) award at IUPUI.
Collaborators & Other Affiliations Collaborators and Co-Editor: M. Babbar-Sebens, J. Bidwell, J. Mostafa, K. S. Narendra, M. Palakal,
Graduate and Postdoctoral Advisors K. S. Narendra, Dept of Elec. Eng, Yale University
196
BIOGRAPHICAL SKETCH Provide the following information for the Senior/key personnel and other significant contributors in the order listed on Form Page 2.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAME
Palakal, Mathew POSITION TITLE
Executive Associate Dean, School of Informaitcs & Computing Professor of Computer Science
eRA COMMONS USER NAME (credential, e.g., agency login)
mpalakal
EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable.)
INSTITUTION AND LOCATION DEGREE
(if applicable) MM/YY FIELD OF STUDY
Concordia University, Montreal, Canada B.S. 05/79 Computer Science
Concordia University, Montreal, Canada M.S. 05/83 Computer Science
Concordia University, Montreal, Canada PhD. 05/87 Computer Science
A. Positions and Honors
Positions and Employment 2013 - present Executive Associate Dean, School of Informatics & Computing
Indiana University Purdue University, Indianapolis 2006 - 2013 Associate Dean, Research & Graduate Programs, School of Informatics
Indiana University Purdue University, Indianapolis 2001 - present Professor of Department of Computer and Information Science
Indiana University Purdue University, Indianapolis 1997 - 2006 Chairman, Department of Computer and Information Science
Indiana University Purdue University, Indianapolis 1994 - 2001 Associate Professor of Computer and Information Science
Indiana University Purdue University, Indianapolis 1988 - 1994 Assistant Professor of Computer and Information Science
Indiana University Purdue University, Indianapolis 1987 - 1988 Visiting Assistant Professor of Computer Science
Concordia University, Montreal, Canada
Other Experience and Professional Memberships (short list) 2001 Reviewer, Bioinformatics 2001 Reviewer, IEEE Transactions on Neural Network 2002-2003 Reviewer, Bioinformatics 2006- Editorial Board, International Journal of Data Mining and Bioinformatics, Inderscience 2005-2008 Bioinformatics Poster Chair for ACM SAC International Symposium 2004-2013 Bioinformatics Track co-chair for ACM SAC International Symposium 2010-2011 ACM International Symposium on Applied Computing, Program Co-Chair 2010 International Symposium in Biocomputing 2010, Program Co-Chair
Honors 2013 Excellence in Service Recognition Award, ACM SIGAPP 2000 Teaching Excellence Recognition Award, Community Learning Network 1998 Teaching Excellence Recognition Award, Computer & Information Science 1997 IUPUI, School of Science Teaching Award 1996 Professor of the Year Award, Computer Science Club 1995 Professor of the Year Award, Undergraduate Programs, Computer Science Club
197
1994 Professor of the Year Award, Computer Science Club 1989 National Science Foundation Research Initiation Award
C. Selected Peer-reviewed Publications (Selected from over 100 peer-reviewed publications)
1. Desai A, Pradhan M, Duraiswamy P, Palakal M. CancerEpiDB: A Database for DNA methylated Genesin Cancer, IEEE International Conference on Data Science and Engineering, Cochin, 2014.
2. Jhamb D, Krishnan A, Palakal M, Pandit y, Duraiswamy P, Palakal M. Identification of ProteinInteraction Methods from Biomedical Literature, 4th IEEE International Conference on ComputationalAdvances in Bio and Medical Sciences (ICCABS), Miami Beach, Fl, 2014.
3. Shabana KM, Nazeer A, Pradhan M, Palakal M. A Computational Method for Drug Repositioning usingPublicly Available Gene Expression Data, 4th IEEE International Conference on ComputationalAdvances in Bio and Medical Sciences (ICCABS), Miami Beach, Fl, 2014.
4. Pradhan, M.P, Desai,A., and Palakal, M.J. Systems biology approach to stage-wise characterization ofepigenetic genes in lung adenocarcinoma, BMC System Biology, (2013). 7:141
5. Mehrabi S, Schmidt M, Waters J, Beesley C, Krishnan A, Kesterson J, Dexter P, AL-Hadad MA, TiernyBW, Palakal M. An efficient pancreatic cyst identification methodology using natural languageprocessing, Studies in Health Technology and Informatics Vol. 192 MEDINFO 2013. Proceedings ofthe 14th World Congress on Medical and Health Informatics Pages 822 – 826, 2013.
6. M. Pradhan, K. A. Prasad, M. J. Palakal, A systems biology approach to the global analysis oftranscription factors in colorectal cancer, BMC Cancer, 12:331, (2012).
7. M. Pradhan, K. Nagulapalli, M. J. Palakal, Cliques for identification of gene signatures for colorectalcancer across population, BMC Systems Biology, (2012). 6(Suppl 3):S17.
8. M. Pradhan, Y. Pandit, L. Ledford, K. Nagulapalli, M. Palakal, A systems biology approach forunderstanding the miRNA regulatory network in colon rectal cancer, International Journal of DataMining and Bioinformatics (in press, 2012).
9. D. Jhamb, N. Rao, D. Milner, F. Song, J. Cameron, D. L. Stocum and M. J. Palakal, Network BasedTranscription Factor Analysis of Regenerating Axolotl Limbs, BMC Bioinformatics, 12:80, 2011.
10. M. Pradhan, L. Ledford, Y. Pandit, and M. Palakal, Global Analysis of miRNA Target Genes in ColonRectal Cancer. IEEE International Conference on Bioinformatics and Biomedicine, Hong Kong, 2010.
11. Y. W. Webster, E. R Dow, J. Koehler, R. C. Gudivada, M. J. Palakal, Leveraging health socialnetworking communities in translational research, Journal of Biomedical Informatics, 44(4):536-44,2011.
12. Y. Webster and M. Palakal, A frame-work for Cross Disciplinary Hypothesis Generation, ACMSymposium on Applied Computing, Sierra, Switzerland, 2010.
13. P. Gandra, M. Pradhan, M. Palakal, Biomedical Association Mining and Validation, ACM InternationalSymposium on Biocomputing, Calicut, 2010.
14. M. Pradhan and M. Palakal, Identifying CRC specific pathways and biomarkers from literatureaugmented proteomics data, BIOCOMP 2010, pp.323-329, 2010.
15. N. Rao, D. Jhamb, DJ. Milner, B. Li, F. Song, M. Wang, S. R. Voss, M. Palakal, M. W. King, B.Saranjami, LD Nye, JA Cameron, and DL. Stocum, Proteomic analysis of blastema formation inregenerating axolotl limbs, BMC Biology, 7:83, 2009. M. Pradhan, P. Gandra, M. Palakal, PredictingProtein-Protein Interactions Using First Principle Methods and Statistical Scoring, ACM InternationalSymposium on Biocomputing, Calicut, 2010.
198
Rajeev R. Raje Professor
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-5174
Web: http://www.cs.iupui.edu/~rraje
E-mail: [email protected]
a. Professional Preparation
b. Appointments
rtment, IUPUI, Indianapolis, IN: Professor, 2009-Present.
-2009.
-2002.
siting Assistant Professor, 1994-1996.
- July 1994.
-Aug 1988.
ect Engineer, 1984-1987.
c. General Summary
Dr. Raje’s expertise is in designing distributed software systems. His current research interests include
service-oriented computing, QoS-aware mobile and distributed systems, and associated software
engineering issues. He has published more than 100 peer-reviewed publications and has also been an
invited speaker on many occasions. His current and past research has been funded, as the PI or Co-PI, by
the Security and Software Engineering Research Center (funding from Air Force Research Labs,
Department of Homeland Security, Lockheed-Martin, and Northrup-Grumman), Office of Naval
Research, National Science Foundation, Microsoft, and Eli Lilly. He, as a PI or Co-PI, has received grant
support worth more than five million dollars. Dr. Raje is a member of the ACM and IEEE.
d. Recent Publications
Relevant
i. Lahiru S. Gallege, Dimuthu U. Gamage, James H. Hill, and Rajeev R. RajeLahiru S. Gallege,
Dimuthu U. Gamage, James H. Hill, Rajeev R. Raje, “Towards Trust-Based Recommender
Systems for Online Software Services”, Proceedings of the 9th Cyber Security and
Information Intelligence Research Workshop, 2014.
ii. Lahiru S. Gallege, Dimuthu Gamage, James H. Hill, Rajeev R. Raje, “Trustworthy Service
Selection using Long-term Monitoring of Trust Contracts”, Proceedings of 17th IEEE
International EDOC Conference, 2013.
iii. Lahiru S. Gallege, Dimuthu U. Gamage, James H. Hill, and Rajeev R. Raje, “Trust Contract
of a Service and its role in Service Selection for Distributed Software Systems”, Proceedings
of the 8th Cyber Security and Information Intelligence Research Workshop, 2013.
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iv. Dimuthu U. Gamage, Lahiru S. Gallege, James H. Hill, Rajeev R. Raje, “Experimental
Evaluation of Trustworthiness of Compositional Systems", Proceedings of the 2nd
International Conference On Network Infrastructure Management Systems, 2013.
v. Dimuthu U. Gamage, Lahiru S. Gallege, James H. Hill, Rajeev R. Raje, “A Compositional
Trust Model for Predicting the Trust Value of Software System QoS Properties”, Proceedings
of the 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing,
2012.
Other Papers
i. Ryan Rybarczyk, Rajeev R. Raje, Mihran Tuceryan, “eDOTS 2.0: A Pervasive Indoor
Tracking System”, Proceedings of SEKE’13, pp. 429-434, Boston, MA, 2013.
ii. Dimuthu U. Gamage, Zhisheng Huang, Andrew Olson, Rajeev R. Raje, “Creating QoS-
aware Distributed Computing Systems Using UniFrame Approach”, Proceedings of the 2nd
International Conference on Computing, 2011.
iii. Rajeev R. Raje, Snehasis Mukhopadhyay, Sucheta Phatak, Rashmi Shastri, Lahiru Gallege,
“Software Service Selection by Multi-Level Matching and Reinforcement Learning”,
Proceedings of the 5th International ICST Conference on Bio-Inspired Models of Network,
Information, and Computing Systems, 2010.
iv. Girish Joshi, Rajeev R. Raje, Mihran Tuceryan, “Designing and Experimenting with a
Distributed Tracking System”, Proceedings of the 14th IEEE International Conference on
Parallel and Distributed Systems, 2008.
v. Omkar J. Tilak, Snehasis Mukhopadhyay, Rajeev R. Raje, Mihran Tuceryan, “A Novel
Reinforcement Learning Framework for Sensor Subset Selection”, Proceedings of IEEE
International Conference on Networking, Sensing, and Control, 2010.
e. Synergistic Activities
i. Reviewer for IEEE Computer, NSF Panels, IEEE Transactions on Software Engineering,
Concurrency and Computation: Practice and Experience, Journal of Parallel and Distributed
Computing and Many Conferences.
g. Collaborators & Other Affiliates
i. Collaborators: M. Tuceryan, S. Mukhopadhyay, A. Olson, and M. Palakal – IUPUI, G. Singh
– KSU, J. Mostafa – UNC, B. Bryant – UNT, J. Gray – UA, M. Auguston – NPS.
ii. Graduate Advisor: Daniel J. Pease – Syracuse University.
iii. Recent Graduate Research Associates: L. Gallege, D. Gamage, R. Rybarczyk, A. Phadke, S.
Sharma, G. Joshi, K. Pradhan, O. Tilak, S. Phatak.
h. Recent Grants
i. A Distributed Framework for Indoor Location Tracking, Purdue Research Foundation,
$17,241, 2013-14 (Joint PI)
ii. Testing-as-a-Service: Static Code Analysis Tool Study – Phases I and II, Lockheed Martin,
Northrop Grumman, and Department of Homeland Security (via S2ERC), $78,163, 2013-14
(Co-PI).
iii. Modeling, Specifying, Discovering, and Integrating Trust into Distributed Real-time and
Embedded (DRE) Systems, Air Force Research Lab (via S2ERC), $79,000, 2011-13 (PI).
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Michele Roberts
Professional Preparation
MBA, Indiana Wesleyan, 1995
MA, Indiana State University (1978)
BS, Central College (1976)
Appointments
April, 2014 – Present, Senior Lecturer, Department of Computer and Information
Science, Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, IN.
January, 1999 – Lecturer, Department of Computer and Information Science, Indiana
University Purdue University Indianapolis (IUPUI), Indianapolis, IN.
August, 1998 – December, 1998 – Visiting Lecturer, Department of Computer and
Information Science, Indiana University Purdue University Indianapolis (IUPUI),
Indianapolis, IN.
Education-Related Honors and Awards
Poster Presentation
Data-Driven Broadened Participation, Presented at SIGSCE 2014
Internal Grants (funded)
Curriculum Enhancement Grant, 2013 (Co-PI)
Gateway Department Grant, 2008-2009 (PI: M Roberts)
Integrative Department Grant, 2007-2008 (PI: M Roberts)
External Grants (funded)
ICHE: Improving Teacher Quality Partnership, 2004-2005, (PI: Kim Nyugen)
External Grants (non- funded)
Improving Teacher Training, 2003, (PI: Kim Nyugen)
A Pilot Program for Accelerating Technology Integration in the K-12
Classroom, 1999 (PI: Mathew Palakal)
Patents
US Patent (Awarded) 09179788 York, Thompson and Roberts: Method and
System for Electronic Re-calibration of an Electronic Control Module.
US Patent (Awarded) 5426585 Stepper, Roberts, et al: Method and Apparatus for
Generating Calibration Information of an Electronic Engine Control Module.
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Fengguang Song Assistant Professor
Department of Computer and Information Science Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-7265 Web: http://www.cs.iupui.edu/~fgsong
E-mail: [email protected] a. Professional Preparation Postdoc Research Associate, Innovative Computing Laboratory, University of Tennessee, 2010-2012.
Ph.D. in Computer Science, University of Tennessee at Knoxville, TN, 2009.
M.S. in Computer Science, University of British Columbia, Vancouver, Canada, 2002.
B.S. in Computer Science, Zhengzhou University, Zhengzhou, China, 1996.
b. Appointments CIS Department, IUPUI, Indianapolis, IN: Assistant Professor, 2013-Present.
Computer Science Lab (CSL), Samsung Research America-Silicon Valley, CA: Senior Researcher,2012-2013.
c. General SummaryDr. Song’s expertise is in parallel, distributed systems, and high performance computing. He currently conducts research at the frontiers of computer science towards exascale computing and big data science discovery across different disciplines. In particular, he focuses on parallel algorithms, software, and advanced architectures for scientific computing, life science, simulation, and knowledge discovery. He designs innovative algorithms and software systems that can scale on large high-end systems with heterogeneous many-cores and accelerators at extreme scale.
d. Recent Publications
Relevant [ICS’14] Fengguang Song and Jack Dongarra, “Scaling Up Matrix Computations on Shared-Memory Manycore Systems with 1000 CPU Cores”, The 28th ACM International Conference on Supercomputing, Munich, Germany, June 2014 (Acceptance rate: 21%).
[UCC’13] Daniel Waddington, Juan Colmenares, Jilong Kuang, Fengguang Song, “KV-Cache: A Scalable High-Performance Web-Object Cache for Manycore”, The 6th ACM/IEEE International Conference on Utility and Cloud Computing, Dresden, Germany, December 2013 (Acceptance rate: 24%).
[ICS’12] Fengguang Song, Stanimire Tomov, Jack Dongarra, “Enabling and Scaling Matrix Computations on Heterogeneous Multi-Core and Multi-GPU Systems”, The 26th ACM International Conference on Supercomputing, San Servolo Island, Venice, Italy, June 2012 (Acceptance rate: 22%).
[SPAA’12] Fengguang Song and Jack Dongarra, “A Scalable Framework for Heterogeneous GPU- Based Clusters”, The 24th ACM Symposium on Parallelism in Algorithms and Architectures, Pitts- burgh, PA, June 2012 (Acceptance rate: 26%).
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[SC’10] Fengguang Song, Hatem Ltaief, Bilie Hadri, Jack Dongarra, “Scalable Tile Communication-Avoiding QR Factorization on Multicore Cluster Systems”, ACM/IEEE Conference on Supercomputing, New Orleans, LA, November 2010 (Acceptance rate: 20%).
Other Papers [SC’09] Fengguang Song, Asim YarKhan, Jack Dongarra, “Dynamic Task Scheduling for Linear Algebra Algorithms on Distributed-Memory Multicore Systems”, ACM/IEEE Conference on Super- computing, Portland, OR, November 2009 (Acceptance rate: 22%).
[CLUSTER’09] Fengguang Song, Shirley Moore, Jack Dongarra, “Analytical Modeling and Optimization for Affinity Based Thread Scheduling on Multicore Systems”, IEEE Cluster Computing 2009, New Orleans, LA, August 2009.
[ICCS’09] Fengguang Song, Jack Dongarra, Shirley Moore, “A Scalable Non-blocking Multicast Scheme for Distributed DAG Scheduling”, The International Conference on Computational Science 2009, LNCS 5544, 195–204, Baton Rouge, LA, May 2009 (Acceptance rate: 29%).
[HPDC’07] Fengguang Song, Shirley Moore, Jack Dongarra, “Feedback-Directed Thread Scheduling with Memory Considerations”, The 16th IEEE International Symposium on High-Performance Distributed Computing, Monterey Bay, CA, June 2007 (Acceptance rate: 20%).
[IWOMP’06] Oscar Hernandez, Fengguang Song, Barbara Chapman, Jack Dongarra, Bernd Mohr, Shirley Moore, Felix Wolf, “Performance Instrumentation and Compiler Optimizations for MPI/OpenMP Applications”, International Workshop on OpenMP, LNCS 4315, 267–278, Reims, France, June 2006.
e. Synergistic Activitiesi. Reviewer for TPDS, JPDC, ParCo, TACO, Supercomputing.ii. Technical program committee members for SC’14, IPDPS’14, EuroMicro’14.iii. Co-chair of International Workshop on High Performance Big Graph Data Management,
Analysis, and Mining (in conjunction with IEEE BigData 2014).
g. Collaborators & Other Affiliatesi. Collaborators: Juan Colmenares (Samsung Research America), Jack Dongarra (UTK), Bilel
Hadri (NICS), Jilong Kuang (Samsung Research America), Hatem Ltaief (KAUST), ShirleyMoore (UT-El Paso), Rajeev Raje (IUPUI), Stanimire Tomov (UTK), Mihran Tuceryan(IUPUI), Daniel Waddington (Samsung Research America), Asim YarKhan (UTK), LuodingZhu (IUPUI).
ii. Graduate Advisor: Jack Dongarra, University of Tennessee at Knoxville.iii. Recent Graduate Research Associates: Jeffery Kriske, Prateek Nagar, Sanjay Akella.
h. Recent Grantsi. Scalable Manycore Software System for Scientific Computing, Grant to Enhance
Interdisciplinary Research and Education, 01/01/2014 - 12/31/2014, Institute of MathematicalModeling and Computational Science (iM2CS), $8,000, IUPUI.
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Gavriil Tsechpenakis Associate Professor
Department of Computer and Information Science
Indiana University-Purdue University Indianapolis (IUPUI)
Web: cs.iupui.edu/~gavriil
E-mail: [email protected]
a. Professional PreparationRutgers University, NJ, Computer Science, Dec 2006
National Technical University of Athens, Greece, PhD, Electr & Comp Eng, Jun 2003
National Technical University of Athens, Greece, Dipl.-Ing., Electr & Comp
Eng, Feb 1999.
b. AppointmentsAssociate Professor (tenured) Jul 2014--present Indiana University-Purdue University Indianapolis, Dept. of Computer and Information Science Assistant Professor (tenure-track) Aug 2010--Jun 2014 Indiana University-Purdue University Indianapolis, Dept. of Computer and Information Science Research Assistant Professor Jul 2008--Jul 2010 University of Miami, FL, Dept. of Computer Science Visiting Assistant Professor Jan 2007--Jun 2008 University of Miami, FL, Dept. of Electrical and Computer Engineering
c. General Summary2013: NSF CAREER Award
2010--2013: Two federal-funded projects (National Science Foundation) as the sole principal investigator (including the CAREER Award), with budget summing up to $900K (secured funding until 2018)
2013: Indiana University Collaborative Research Grant (Funding rate ~5%) 2010: Two federal-funded subcontracts (National Institutes of Health) transferred to
IUPUI from University of Miami 2008-2012: Completed five fully funded projects
2010--2013: Four invited talks (not including job interviews and conference presentations) 2007: Best reviewer’s award at the most significant Computer Vision conference (Int’l
Conference of Computer Vision) 1999--2013: 20 journal and 35 peer review conference papers (all “top-tier”), 7 book
chapters, 8 abstracts
d. Recent PublicationsX. Chang, M.D. Kim, R. Stephens, T. Qu, A. Chiba, and G. Tsechpenakis, “Part-based Motor Neuron
Recognition in the Drosophila Ventral Nerve Cord,” Elsevier NeuroImage, 90:33-42, 2014.
X. Chang, M.D. Kim, R. Stephens†, T. Qu, A. Chiba, and G. Tsechpenakis, “Neuron Recognition with
Hidden Neural Network Random Fields,” Int'l Symposium on Biomedical Imaging: from Nano to Macro
(ISBI), Beijing, China, 2014.
S. Farhand, F.M. Andreopoulos, and G. Tsechpenakis, “CRF-driven Multi-compartment Geometric
Model,” Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), 2013.
X. Chang, M.D. Kim, A. Chiba, and G. Tsechpenakis, "Motor Neuron Recognition in the Drosophila
Ventral Nerve Cord," Int'l Symposium on Biomedical Imaging: from Nano to Macro (ISBI), 2013.
G. Tsechpenakis, P. Mukherjee, M. D. Kim, and A. Chiba, "Three-dimensional Motor Neuron Morphology
Estimation in the Drosophila Ventral Nerve Cord," IEEE Trans on Biomed Eng., 59(5):1253-63, 2012.
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X. Chang, M.D. Kim, A. Chiba, and G. Tsechpenakis, "Patterning Motor Neurons in the Drosophila
Ventral Nerve Cord using Latent State Conditional Random Fields," Int'l Symposium on Biomedical
Imaging: from Nano to Macro (ISBI), 2012.
S.P. Chatzis, G. Tsechpenakis, “A Possibilistic Clustering Approach Towards Generative Mixture
Models,” Pattern Recognition,45(5):1819-25, 2012.
G. Tsechpenakis and S. Chatzis, “Deformable Probability Maps: Probabilistic Shape and Appearance-
based Object Segmentation,” Computer Vision and Image Understanding, 115(8):1157-69, 2011.
S. Chatzis† and G. Tsechpenakis, “The Infinite Hidden Markov Random Field Model,” IEEE Trans. on
Neural Networks, 21(6):1004-14, 2010.
G. Tsechpenakis and D. Metaxas, “CoCRF Deformable Model: A Geometric Model Driven by
Collaborative Conditional Random Fields,” IEEE Trans. on Image Processing, 18(10):2316-29, 2009.
e. Synergistic ActivitiesIndianapolis Project Seed internships for K-12, 2012--present
National Science Foundation, Panel reviewer, 2010
Int’l Symposium on Visual Computing, Workshop organizer, 2010; Reviewer
(2006--present): IEEE Trans Pattern Anal. and Machine Intel, IEEE Trans on Image Proc, IEEE Trans. on
Biomedical Eng., IEEE Trans on Systems Man and Cybernetics, Image and Vision Computing Journal,
Elsevier Medical Image Analysis, Elsevier Computer-Aided Design, Journal of Medical Physics, Journal of
Neurophysiology, Int’l Conference on Computer Vision, European Conference on Computer Vision, IEEE
Computer Vision and Pattern Recognition, Med Image Comp & Comp.-Assisted Intervention, Int’l
Symposium on Biomedical Imaging, Int’l Symposium on Visual Computing
g. Collaborators & Other AffiliatesAkira Chiba (Biology, University of Miami), Vincent Lemmon (Neurological Surgery, University of Miami), Michael Kim (Molecular and Cellular Pharmacology, University of Miami), Brian Samuels (Glick Eye Institute, Indiana University), Kenneth Muller (Physiology and Biophysics, University of Miami), Laura Bianchi (Physiology and Biophysics, University of Miami), Larry Cohen (Physiology, Yale University School of Medicine), John Nicholls (Scuola Internazionale Superiore di Studi Avanzati, Italy), Jaime Eugenin (Biology, Universidad de Santiago de Chile), Fotios Andreopoulos (Biomedical Engineering, University of Miami), Radka Stoyanova (Radiation Oncology, University of Miami), Meng Lin (Optometry, University of California, Berkeley), Brandon Lujan (Ophthalmology, University of California, Berkeley), Philip Rosenfeld (Bascom Palmer Eye Institute, University of Miami), Jianhua Wang (Bascom Palmer Eye Institute, University of Miami), Shuliang Jiao (Bascom Palmer Eye Institute, University of Miami), Monica Driscoll (Molecular Biology and Biochemistry, Rutgers University), Robert Cowen (Rosenstiel School of Marine & Atmospheric Science, University of Miami), Carol Neidle (Modern Foreign Literatures, Boston University).
h. Recent GrantsCAREER: Modeling the Structure and Dynamics of Neuronal Circuits in the Drosophila larvae
using Image Analytics, NSF (DBI), PI: Tsechpenakis, 573K, Jun 2013--May 2018.
ABI Innovation: Modeling the Drosophila Brain with Single-neuron Resolution using Computer
Vision Methods, NSF (DBI), PI: Tsechpenakis, 319K, May 2011--Apr 2014.
A Novel Retinal Imaging Approach to Diagnose Glaucoma, Indiana University (IUCRG), PI:
Tsechpenakis, May 2013--Apr 2014.
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Curriculum Vitae Mihran Tuceryan
Department of Computer & Information Science
Indiana University Purdue University Indianapolis
723 W. Michigan St, SL-280K
Indianapolis, IN 46202-5132
Phone: (317) 274-9736
Email: [email protected]
Education
Ph.D., Computer Science, University of Illinois, Urbana, 1986
B.S., Computer Science and Engineering, Massachusetts Institute of Technology, 1978
Professional Experience
• Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, Indiana, Professor, (August 2012 –
Present)
• Indiana University Purdue University Indianapolis (IUPUI), Indianapolis, Indiana, Associate Professor,
(March 1997– July 2012).
• Technical University of Munich (TUM), Munich, Germany, Visiting Professor, (July – December 2004), (on
sabbatical leave from IUPUI).
• Texas Instruments, Dallas, Texas, Member of Technical Staff, (October 1995 – March 1997).
• European Computer-Industry Research Centre (ECRC), Munich, Germany, Senior Research Scientist,
(September 1992 – September 1995).
• Michigan State University, East Lansing, Michigan, Assistant Professor in Computer Science, (September 1986
– August 1992).
Honors
2005 Senior Member, Institute of Electrical and Electronics Engineers (IEEE) 2006 IU Trustees Teaching Award (TTA), Indiana University Patents
US Patents Awarded
1. US Patent #6044168: Model based faced coding and decoding using feature detection and eigenface
coding, M. Tuceryan, B. Flinchbaugh, 2000.
2. US Patent #6753828, System and method for calibrating a stereo optical see-through head-mounted
display system for augmented reality, M. Tuceryan, N. Navab, Y. Genc, 2004.
3. US Patent #7190331: System and method for measuring the registration accuracy of an augmented
reality system, Y. Genc, N. Navab, M. Tuceryan, E. McGarrity, 2007.
Selected Peer-reviewed publications(reverse chronological order)
1. Aboli Phadke, Ryan Rybarczyk, Rajeev R. Raje, Mihran Tuceryan, “Incorporating Mobile Devices
in Indoor Tracking ,” 3rd International Conference On Network Infrastructure Management
Systems, Mumbai, India, 2014.
2. Chouvatut, V.; Madarasmi, S. & Tuceryan, M. “3D face and motion estimation from sparse points
using adaptive bracketed minimization,” Multimedia Tools and Applications, Springer US, vol. 63,
pp. 569-589, 2013.
3. Ruwan Egoda Gamage, Abhishek Joshi, Jiang Yu Zheng, Mihran Tuceryan. “A High Resolution
3D Tire and Footprint Impression Acquisition for Forensics Applications,” Proc. of Workshop on
Applications of Computer Vision (WACV), Clearwater, Florida, pp. 317-322, January, 2013.
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4. Chouvatut, V.; Madarasmi, S.; Tuceryan, M. “3D Face and Motion from Feature Points Using
Adaptive Constrained Minima,” IEICE Transactions on Fundamentals of Electronics,
Communications and Computer Sciences, vol. E94.A, pp. 2207-2219, 2011.
5. Jazayeri, A.; Cai, H.; Zheng, J. Y., Tuceryan, M. “Vehicle Detection and Tracking in Car Video
Based on Motion Model,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2,
pp. 585-595, 2011.
6. Mihran Tuceryan, Fang Li, Herbert L. Blitzer, Edwin T. Parks, Jeffrey A. Platt, “A Framework for
Estimating Probability of a Match in Forensic Bite Mark Identification,” Journal of Forensic
Sciences, vol. 56, pp. S83–S89, 2011.
7. A. Jazayeri, H. Cai, M. Tuceryan, and J.Y. Zheng, “Smart video systems in police cars”,
Proceedings of the international conference on Multimedia, pp. 807–810, Firenze, Italy, October
25-29, 2010.
8. Amirali Jazayeri, Hongyuan Cai, Jiang Yu Zheng, Mihran Tuceryan, “Identifying Vehicles in
In-Car Video Based on Motion Model,” IEEE Intelligent Vehicles Symposium (IV 2010), pp. 493
– 499, San Diego, June 2010.
9. Varin Chouvatut, Suthep Madarasmi, and Mihran Tuceryan, “3D Reconstruction and Camera Pose
from Video Sequence Using Multi-dimensional Descent,” in 4th International Conference on
Information Systems, Technology and Management, Bangkok, Thailand, March 11-13, 2010,
ICISTM 2010, vol. 54, pp. 282–292, ISSN: 1865-0929 (Print) 1865-0937 (Online).
10. Varin Chouvatut, Suthep Madarasmi, and Mihran Tuceryan, “Face Reconstruction and Camera
Pose Using Multi-dimensional Descent,” in Proceedings of the International Conference on
Computer, Electrical, and Systems Science, and Engineering (CESSE 2009 ), World Academy of
Science, Engineering and Technology Volume 60, December 2009, pp. 730–735, Bangkok,
Thailand, December 25-27, 2009, ISSN: 2070-3724.
11. Glenn Flora, Mihran Tuceryan, Herbert Blitzer, “Forensic Bite Mark Identification Using Image
Processing Methods,” in Proceedings of the 24th Annual ACM Symposium on Applied Computing
(SAC), pp. 903-907, Honolulu, Hawaii, March 2009.
12. Amirali Jazayeri, Hongyuan Cai, Jiang Yu Zheng, Mihran Tuceryan, Herbert Blitzer, “An
Intelligent Video System for Vehicle Localization and Tracking in Police Cars,” in Proceedings of
the 24th Annual ACM Symposium on Applied Computing (SAC), pp. 939-940, Honolulu, Hawaii,
March 2009.
Current and Past Research Support
1. Mihran Tuceryan (PI) and Jiang Y. Zheng (co-PI), “Digitizing Device to Capture Track
Impressions,” National Institute of Justice (NIJ), Sep 2012 – Feb 2013, $253,120.
2. Mihran Tuceryan (PI) and Jiang Zheng (co-PI), “Advanced In-Car Video System,” subcontract
from Institute for Forensic Imaging (IFI), $96,743. Original grant from National Institute of Justice
to IFI for $270,000, 2007–2009.
3. Mihran Tuceryan (PI) and A. K. Jain (co-PI), “Integration of Perceptual Grouping Modules with
3D Interpretation Modules,” National Science Foundation, February 1992 (for two years),
$233,401.
4. Mihran Tuceryan (PI) and A. K. Jain (co-PI), “Perceptual Grouping in Computer Vision,” National
Science Foundation, June 1987 – December 1989, $156,761.
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Xukai Zou Associate Professor
Department of Computer and Information Science Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 278-8576 Web: http://www.cs.iupui.edu/~xkzou
E-mail: [email protected] a. Professional PreparationUniversity of Nebraska-Lincoln, USA Computer Science Ph.D., 2000 Huazhong University of Science & Technology, China Computer Science M.S., 1986 Zhengzhou University, China Computer Science B.S., 1983 b. Appointments
July 2009 - present, Associate Professor, Department of Computer Science, IUPUI August 2003 – June 2009, Assistant Professor, Department of Computer Science, IIUPUI 1993 - 1997 Associate Professor, Dept. of Computer Science, Zhengzhou University, P.R. China. 1986 - 1992 Lecturer/Assistant Professor, Dept. of Computer Science, Zhengzhou University, P.R. China. Other Professional Appointments 2002 - 2003 Post doctoral research associate and Lecturer, Department of Computer Science & Engineering, University of Nebraska- Lincoln, Lincoln, NE, USA. 2001 - 2002 Software Architect, ACE information resource Inc., NJ, USA.
c. General Summary
My area of expertise is cryptography and network security, particularly, group key management, secret sharing, access control, biometrics and user authentication, moving target defense, secure digital provenance, secure electronic voting, health information security and personal genomic data privacy, and social, delay tolerant, and mobile security. My research is mainly driven by fundamental security needs in computing/networking systems and real applications such as user authentication, medical/genomic data security/privacy and secure online voting and election. I have published over 90 peer-reviewed papers, including 5 book chapters and 45 peer-reviewed papers since 2007, and also three monographic books. My research has been supported by National Science Foundation, the Department of Veterans Affairs, and Industry such as Cisco and Northrop Grumman. I have served as associated editor for three international journals, and program co-chair, program committee member, and reviewer for a lot many international conferences and journals. I also served on NSF panelist and as panel reviewer for NIH.
d. Recent Publications (* indicates student authors)1. X. Zou, H. Li*, Y. Sui*, W. Peng*, and F. Li, Assurable, Transparent, and Mutual Restraining
E-voting Involving Multiple Conflicting Parties, INFOCOM'2014, April 28 to May 2, Toronto,Canada, pp. 136-144, (Acceptance rate: 19%).
2. Y. Sui*, X. Zou, Y. Du, and F. Li, Design and Analysis of a highly user-friendly, secure, privacy-preserving, and revocable authentication method, IEEE Transactions on Computers, 63(4), pp.902-916, April 2014.
3. W. Peng*, F. Li, X. Zou, and J. Wu, A Two-stage Deanonymization Attack AgainstAnonymized Social Networks, IEEE Transactions on Computers, 63(2), pp. 290--303,2014.
4. W. Peng*, F. Li, X. Zou, and J. Wu, Behavioral Malware Detection in Delay TolerantNetworks, IEEE Transactions on Parallel and Distributed Systems, 25 (1), pp. 53--63,2014.
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5. M. Rangwala*, P. Zhang, X. Zou and F. Li, A Taxonomy of Privilege Escalation Attacks inAndroid Applications, International Journal of Security and Networks, Vol. 9, No. 1, 2014, pp.40--55.
6. H. Li*, Y. Sui*, W. Peng*, X. Zou, and F. Li, A Viewable E-voting Scheme for Environmentswith Conflict of Interest, IEEE Conference on Communications and Network Security, Oct. 14--16, 2013 Washington, D.C., USA, pp. 251—259.
7. Y. Sui*, X. Zou, F. Li and E. Y. Du, Active User Authentication for Mobile Devices,IEEE WASA’2012, Volume 7405, Page(s): 540-548.
8. Y. Sui*, X. Zou and E. Y. Du, Biometrics-based authentication: a new approach, Proceedings ofICCCN’11, July 31—August 4, 2011, Hawaii, USA, Session: Biometric Security and OnlineApplication:1--9.
9. X. Zou, F. Maino, E. Bertino, Y. Sui*, K. Wang*, and F. Li, A New Approach to WeightedMulti-Secret Sharing, ICCCN’11, July 31 - August 4, 2011, Hawaii, USA.
10. X. Zou, Y. Dai and E. Bertino, A Practical and Flexible Key Management Mechanism ForTrusted Collaborative Computing. Proceedings of INFOCOM'08, April 2008, Phonex, Arizona,USA, pages 538–546, (Acceptance rate: 20%).
e. Synergistic Activities
• NSF Panelist (2008, 2010) and NIH grant external reviwer.• Contribute to the designation of National Centers of Academic Excellence in Information
Assurance Education (2007 by NSA) and Research (2008, by NSA and DHS).• Director of TEGO (Trusted Electronics and Grid\&Group Obfuscation) Research and Education
Center• Associate Editor (AE) of International Journal of Computers and Applications (2003-), Associate
Editor (AE) of International Journal of Security and Networks (2009-), Associate Editor of theJournal Communications (2010-), Program Co-Chair for three International Conferences andreviewer or TPC member for many International Journals and Conferences such as IEEETransactions on Computers, IEEE Transactions on Dependable and Secure Computing, IEEETransaction on Parallel and Distributed Computing, ACM Transactions on Information andSystem Security.
g. Collaborators & Other Affiliates(i) Collaborators: Peng Liu, Penn State University, Eliza Yingzi Du, QualComm, Jake Chen, Indiana University, Feng Li, IUPUI, Li Bai, Temple University, Jie Wu, Temple University, Elisa Bertino, Purdue University, Fabio Maino, Cisco System Inc. (ii) Graduate and Postdoctoral Advisors: Postdoctoral advisor : Prof. Byrav Ramamurthy, University of Nebraska-Lincoln. Ph.D. advisors: Profs. Spyros Magliveras and Byrav Ramamurthy, University of Nebraska-Lincoln.
h. Recent Grants1. NSF #1262984, REU Site: Enhancing Undergraduate Experience in Mobile ComputingSecurity, 6/1/2013-5/31/2016, Co-PI. 2. Northrop Grumman, MovingCloud: Create Moving-target Defense in Cloud by Learningfrom Botnets, 10/1/2012--9/31/2013, Co-PI. 3. CISCO, Building A Secure Video Streaming Framework for Dynamic and AnonymousSubscriber Groups, 07/15/08--07/15/09, Sole PI. 4. NSF #CCR-0311577, Secure Group Communications over Wired/Wireless Networks,9/1/2003 – 8/31/2007, PI (at IUPUI).
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NAME
Yuni Xia
CONTACT INFORMATION Department of Computer and Information Science
Indiana University - Purdue University Indianapolis (IUPUI)
723 W. Michigan Street, SL 280, Indianapolis, IN 46202
Phone: 317-274-9738, Fax: 317-274-9742
Email: [email protected]
EDUCATION Ph.D., Computer Science, Purdue University, 2005.
M.S., Computer Science, Purdue University, 2002
B.S., Computer Science and Engineering, Huazhong University of Science and Technology, China, 1996
ACADEMIC APPOINTMENTS 2012 – Present Associate Professor of Computer and Information Science, IUPUI
2005 – 2012, Assistant Professor of Computer and Information Science, IUPUI
2003 Research Intern, IBM TJ Watson Research Center, NY
1999 – 2005 Research Assistant, Purdue University
AWARDS AND HONORS 1. Best Demo Award, International Conference on Database System for Advanced Application (DASFAA),
2011
2. Scalable Data Analytics Innovation Award, IBM, 2010
3. Techpoint Mira Award, with Senior Care Navigation System development team at My Health Care Manager
LLC, Indiana TechPoint Organization, 2010
4. Research Venture Award, IUPUI, 2009
5. Trustee’s Teaching Award, IUPUI, 2009
6. Real Time Innovation Award, IBM, 2008
GRANTS, FELLOWSHIPS AND AWARDS
1. Health-Terrain: Visualizing Large Scale Health Data, Supported by US Department of the Army, Co-PI (PI:
Fang), 2013-2015
2. Development of Key Technologies for Big Data Analysis and Management Software Based on Next
Generation Memory, Supported by ETRI, Co-PI (Institute PI: Lee), 2012-2017.
3. Large Scale Sensor Stream Analysis and Mining for Geriatric Care, PI, IBM Research, 2010.
4. DisProt Database: A Central Repository of Information on Intrinsically Disordered Proteins, Co-PI (one
month summer and one graduate student, PI: Keith Dunker), National Science Foundation(NSF), 2009-
2012.
5. TrafficAnalyzer: A Real-time Traffic Stream Processing and Analyzing System, PI, IBM Research, 2008.
6. Invention of a Consumer-Side Geriatric Health Care Knowledge Management and Decision Support
System, Co-PI (2.4 month summer and 1 graduate student, PI: My Health Care Manager LLC), 21st Century
Research and Development Fund, State of Indiana, 2008-2010.
7. NSF-EHCS (EHS), SM: Development of SYMBIOTE; A Reconfigurable Logic Assisted Data Stream
Management System for Multimedia Sensor Networks, Co-PI (0.3 month summer and one graduate student,
PI: Jaehwan Lee), National Science Foundation(NSF), 2008-2010.
Recent Publications ( from 46 publications)
1. Biao Qin, Yuni Xia, Fang Li and Jiaqi Ge, EMU: An expectation maximization based approach for
clustering uncertain data, Journal of Intelligent & Fuzzy Systems, 1067- 1083, 2013.
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2. Chandima Hewa Nadungodage, Yuni Xia, John Lee, Myungcheol Lee, Choon Seo Park, GPU Accelerated
Item-Based Collaborative Filtering for Big-Data Applications, proceedings of the IEEE International
Conference on Big Data (IEEE BigData) 2013.
3. Chandima Hewa Nadungodage, Yuni Xia, Jaehwan John Lee, Yi-cheng Tu, Hyper-Structure Mining of
Frequent Patterns in Uncertain Data Streams, Journal of Knowledge and Information Systems ( KAIS) ,
2013.
4. Biao Qin, Yuni Xia, Shan Wang, Xiaoyong Du, A Novel Bayesian Classification for Uncertain Data,
Knowledge-Based Systems, Journal of Knowledge-Based Systems, Volume 24, Issue 8, 1151-1158 , 2011.
5. Yicheng Tu, Shaoping Chen, Yuni Xia, Performance Analysis of A Dual-Tree Algorithm for Computing
Spatial Distance Histograms, The VLDB Journal. 20(4):471-494, 2011.
6. Biao Qin, Yuni Xia, Sunil Prabhakar, Rule induction for uncertain data, Knowledge and Information
Systems, Knowledge and Information Systems - KAIS , vol. 24, no. 2, 2010 .
7. Jiaqi Ge, Yuni Xia, A Discretization Algorithm for Uncertain Data, the 21st International Conference on
Database and Expert Systems Applications (DEXA), 2010.
8. Pranav S. Vaidya, Jaehwan John Lee, Francis Bowen, Yingzi Du, Chadima H. Nadungodage, Yuni Xia.
Symbiote - A Reconfigurable Logic Assisted Data Stream Management System, ACM SIGMOD
Conference, Demo, 2010.
9. Jiaqi Ge, Yuni Xia, Chandima Hewa Nadungodage. Classify Uncertain Data with Neural Network, the 14th
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2010.
10. Biao Qin, Yuni Xia, Rakesh Sathyesh, Sunil Prabhakar, Yicheng Tu, uRule: A Rule Based Classifier for
Data with Uncertainty, the IEEE International Conference on Date Mining (ICDM), 2009.
TEACHING
CSCI340: Discrete Computational Structures
CSCI441: Client Server Databases
CSCI443: Database Systems
CSCI481: Introduction to Data Mining
CSCI541: Database Management Systems
CSCI573: Data Mining
CSCI590: Advanced Database Systems
SERVICES
Program Committee on more than 10 Conferences,
Panelist, National Science Foundation, CISE, 2007, 2009, 2011
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Jiang Yu Zheng Professor
Department of Computer and Information Science Indiana University-Purdue University Indianapolis (IUPUI)
Phone: (317) 274-3883 Web: http://www.cs.iupui.edu/~jzheng
E-mail: [email protected]
a. Professional Preparation
• 1983, BS, Computer Science, Fudan University, China• 1987, MS, Control Engineering, Osaka University, Japan• 1990, PhD, Control Engineering, Osaka University, Japan
b. Appointments
• 1990-1993, Research Associate, ATR Communication System Lab, Advanced TelecommunicationResearch Institute, Japan
• 1994-2001, Associate Professor, School of Information Science and Engineering, Kyushu Institute ofTechnology, Japan
• 2001-2011, Associate Professor, Department of Computer Science and TASI, IUPUI• 2011-Current, Professor, Department of Computer Science, IUPUI
c. General Summary
Dr. Zheng works in the areas of image, video, multimedia, computer vision, virtual reality, pervasive computing, and intelligent transportation systems. His current research interests include 3D measuring and modeling, dynamic image processing and tracking, scene representation for various environments, intelligent vehicle, and sensor network. His research was supported by NIJ, NICT and TOYOTA. Dr. Zheng has published 150 papers in journals and conferences as main author and he is a senior member of IEEE.
d. Recent Publications
1. S. Bagheri, JY Zheng, Temporal mapping of surveillance video, International Conference on PatternRecognition, 2014, 1-6.
2. S. Bagheri JY Zheng, Localized temporal profile of surveillance video, IEEE International Conference onMultimedia and Expo. 2014, 1-6.
3. M Kilicaslan, JY Zheng, Visualizing driving video in temporal profile, IEEE Intelligent VehicleSymposium, 2014, 1-7.
4. D Gong, JY Zheng, A Maximum Correlation Feature Descriptor for Heterogeneous FaceRecognition 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR), 135-139, 2013
5. S Wang, S Luo, Y Huang, JY Zheng, P Dai, Q Han, Railroad online: acquiring and visualizing routepanoramas of rail scenes, The Visual Computer, 2013, 1-13.
6. S Wang, JY Zheng, S Luo, X Luo, Y Huang, D Gao, Route panorama acquisition and rendering for high-speed railway monitoring, IEEE International Conference on Multimedia and Expo (ICME), 2013, 1-6.
7. RE Gamage, A Joshi, JY Zheng, M Tuceryan, A high resolution 3D tire and footprint impressionacquisition for forensics applications, IEEE Workshop on Applications of Computer Vision (WACV), 2013,317-322.
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8. A Jazayeri, H Cai, JY Zheng, M Tuceryan, Vehicle detection and tracking in car video based on motionmodel, IEEE Transactions on Intelligent Transportation Systems, 12 (2), 583-595.
9. M Kilicarslan, JY Zheng, Towards collision alarming based on visual motion, 15th IEEE Int. Conf.Intelligent Transportation Systems (ITSC), 2012, 654-659.
10. H Cai, JY Zheng, Automatic heterogeneous video summarization in temporal profile, 21st InternationalConference on Pattern Recognition (ICPR), 2012, 2796-2800.
11. B Zhang, Y Kado, K Hattori, JY Zheng Digital Scope on 2D Communication Sheet for Location-SpecificMultimedia Service, Interactive Multimedia, 2012
12. H Cai, JY Zheng, Video anatomy: cutting video volume for profile, 19th ACM International Conferenceon Multimedia, 1065-1068, 2011.
e. Synergistic Activities
i. Reviewer for ACM TOMCCAP, IEEE MM, IEEE VCG, IJCV, VCIP, CVIU, IPSJTrans. PVA, MVA, IEEE Trans. ITS, Sensor, IEICE, JMPE, IJHC, CAVW, DigitalContent Technology and Application, Int. Journal on Wireless and Mobile Computing
ii. Program committee for ICPR, ICME, ACM MM, CYBERWORLD, IROS, IEEE ICRA,ACCV, ACPR, VSMM, OMNIVISION, Digital Heritage.
iii. Grant review NSF Panels, NPRP, US-Israel Binational Science Foundationiv. Hosting international researchers: Yaping Huang, Shengchun Wang, Seng Luo, Ryo
Fujishiro, Koki Ishida
g. Collaborators & Other Affiliates
• Yaobin Chen, Lauren Christopher, Stanley Chien, TASI, IUPUI• Mihran Tuceryan, CS Department, IUPUI• Chen Yu, Department of Cognitive Science, IUB• Bing Zhang, NICT, Japan
• Transportation Active Safety Institute, IUPUI• Invited research professor at National Inst. of Inf. and Comm. Tech., Japan 2009• Invited professor, Osaka University, Japan in 2007
h. Recent Grants
NIJ: (1) Co-PI, 2008-2009, Advanced In-car Video System, $270,000 (2) Co-PI, 2010-1013, Device to digitize track impression, $253,000
NICT: PI, Sensor Network over 2D Communication LAN Sheet, $50,000 TOYOTA: Co-PI, 2014-2016 Vehicle testing scenario generation $1,500,000
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