NSF/DHS FODAVA-LEAD:Missions and Plans

Haesun ParkComputational Science and Engineering Division

Georgia Institute of Technology

FODAVA Kick-off Meeting, September 2008

Data and Visual Analytics (DAVA)

Analytical

Reasoning

Data Representation

and Transformation

Visual Representation and Interaction Production, Presentation,

Dissemination

Data and Visual Analytics (DAVA)Analytical Reasoning• Apply human judgment to

reach conclusions• Methods to maximally utilize

human capacity to derive deep understanding and insight into complex situations in a minimum amount of time

Data Representation and Transformation• Representing dynamic, incomplete, conflicting data

to convey important content in a form and level of abstraction appropriate to the analytical task to enable understanding

• Transforming data among possible representations to support analysis and discovery

Visual Representation and Interaction

• Visual presentation of information in ways that instantly convey important content taking advantage of human vision

• Interaction techniques (e.g., search) between the analyst and data to facilitate the analytical reasoning process

Production, Presentation, Dissemination• Seamless integration of data acquisition,

analysis, decision making, and action

A Discipline in Data & Visual Analytics

FODAVA is concerned with defining the mathematical and computational foundations for the Data and Visual Analytics Discipline

FoundationsFoundations

AnalyticalReasoning

DataRepresentation

and Transformation

ProductionPresentation and

Dissemination

VisualRepresentationand Interaction

I think, therefore

I am.

I think, therefore

I am.

“Solving a problem simply means representing it so that the solution is obvious.” Herbert Simon, 96

Applications

• FODAVA team will perform foundational research that can be applied to many different fields

– Common end objective is to apply knowledge in decision making process, at the time and place that a decision is needed.

– Common challenges across applications as well as application specific challenges

Social NetworksBiometric RecognitionText Analysis

Bioinformatics

Epidemiology

Homeland Security

Medical Informatics :Theory and practice of knowledge integration, management and use in healthcare delivery, med,public health

Astrophysics

VISION: Establishing DAVA as a Distinct Discipline

• Develop FODAVA community, engage larger DAVA field– Researchers– Educators– Practitioners

• Establish Body of Knowledge– Foundations, subareas,

applications– Curriculum– Education programs

Data Analytics Visualization

Mathematical and Computational FoundationsMathematical and Computational FoundationsData and Visual Analytics

Analytic Reasoning

Production,

Presentation,

Dissemination

Data and Visual Analytics Communities

National Visualization and Analytics Center

(NVAC)/VAC Consortium

National Visualization and Analytics Center

(NVAC)/VAC Consortium

RVAC/

DHS Science & Technology Center

of Excellence

RVAC/

DHS Science & Technology Center

of Excellence

FODAVAFODAVA leadFODAVA partners (08, 09,

…)

FODAVAFODAVA leadFODAVA partners (08, 09,

…)

FODAVA will interact with several

communities of researchers & practitioners

“This partnership with NSF is the most important event since the creation of NVAC in March 2004. It brings to the front stage efforts by folks within DHS, NVAC and NSF to jointly fund the development of basic research in visual analytics supporting DHS applied mission needs.”~Jim Thomas, NVAC Director

FODAVA-Lead Mission

• Research and Education: Serve as a central facility that will involve all FODAVA awardees in a common effort to develop the scientific foundations for data and visual analytics

• Effective Liaison between FODAVA Researchers and NVAC: Interface with DHS NVAC/RVAC and DHS S&T Center of Excellence in research and educational opportunities

• Community Building: Integrate diverse DAVA communities and reach out for broader participation

FODAVA-Lead Challenges

Research and Collaboration• Creation of the Mathematical and Computational

Sciences Foundations required to represent and transform all types of digital data in ways to enable efficient and effective Visualization and Analytic Reasoning

• Intrinsic Challenges: Data sets massive, heterogeneous, multi-dimensional, dirty, incomplete, time-varying; solutions must be produced with time and space constraints, ….

• Understanding Fundamental issues/needs in VA and Communicating results– Isolated theoretical research is not enough– Problem driven foundational research is needed

FODAVA-Lead Challenges (cont’d)

• Education and Research– Defining Foundations of Data and Visual Analytics – Undergraduate and Graduate Curriculum (core

body of knowledge) for Data and Visual Analytics

• Community Building/Integration– A community of researchers who claim DAVA as

their own discipline and FODAVA an essential part

– Conferences, journals, books, professional society engagement,

– Industry, tech transfer, …

FODAVA-Lead PIs at GAtech

Alex GrayCSE

Machine LearningFast Algorithms for Massive DA

Haesun ParkDirector

CSE, Associate ChairNumerical Computing

Data AnalysisResearch, FODAVA Community Building

Vladimir KoltchinskiiMathematics

Machine Learning TheoryComputational Statistics

John StaskoAssociate DirectorIC, Associate ChairSRVAC Co-Director

Information Vis.Collaboration with NVAC and RVACs

Liaison with Vis. community

Renato MonteiroISyE

Continuous OptimizationStatistical Computing

FODAVA-Lead Senior Personnel

James Foley Associate Dean CoC

Graphics and Visualization, HCIVisual Analytics Digital Library

Richard FujimotoAssociate Director

CSE, ChairModeling and Simulation Education and Outreach

Guy LebanonCSE

Machine LearningComputational Statistics

Arkadi NemirovskiISyE

OptimizationNon-parametric Stat.

Alexander ShapiroISyE

Stochastic ProgrammingOptimization

Multivariate Stat. Analysis

Santosh VempalaCS

Theory of ComputigDirector of ARC

Hongyuan ZhaCSE

Numerical ComputingData Analysis

Director of Graduate Studies

Hao-Min ZhouMathematics

Wavelet and PDEImage Processing

2008 FODAVA Partners• Global Structure Discovery on Sampled Spaces

Leonidas Guibas and Gunnar Carlsson (Stanford University)

• Visualizing Audio for Anomaly Detection

Mark Hasegawa-Johnson, Thomas Huang, Hank Kaczmarski, Camille Goudeseune (University of Illinois Urbana-Champaign)

• Principles for Scalable Dynamic Visual Analytics

H. Jagadish, and George Michailidis (University of Michigan)

• Efficient Data Reduction and Summarization

Ping Li (Cornell University)

• Uncertainty-Aware Data Transformations for Collaborative Reasoning

Kwan-Liu Ma (UC Davis)

• Mathematical Foundations of Multiscale Graph Representations and Interactive Learning

Mauro Maggioni, Rachael Brady, Eric Monson (Duke University)

• Visually-Motivated Characterizations of Point Sets Embedded in High-Dimensional Geometric Spaces

Leland Wilkinson and Robert Grossman (University of Illinois Chicago)

Adilson Motter (Northwestern University)

Expertise of FODAVA team

Machine LearningMachine Learning

OptimizationOptimization

Information VisualizationInformation Visualization

SimulationSimulationHuman Computer Interaction

Human Computer Interaction

Computational Math&StatisticsComputational Math&Statistics

GamingGaming

DatabaseDatabase

Real-time Systems

Real-time Systems

Discrete/Graph Algorithms

Discrete/Graph Algorithms

Speech Recognition

Speech Recognition

High Performance Computing

High Performance Computing

Graphics and Vis. Graphics and Vis.

Information Retrieval

Information Retrieval

Numeric & Geometric Computing

Numeric & Geometric Computing

FODAVA Activities• Body of Knowledge

– Curriculum development– Repository for education materials– Distinguished lecture series– Outreach to underrepresented groups

• Community Development– Communications: project description and results– FODAVA web site

• Repository of FODAVA data sets and results

– Conferences and meetings• Annual FODAVA Workshop • NVAC Consortium meetings• Activities at established meetings• Meetings to establish new research directions

Curriculum Development

• Goals– Identify and catalog curriculum development efforts in

Data and Visual Analytics• Individual courses, minors, degree programs• Undergraduate and graduate level

– Leverage existing efforts (e.g., RVAC)– Share experiences, develop best practices– Develop curriculum recommendations

• Curriculum workshop– POCs: Cook (NVAC), Fujimoto (FODAVA), Stasko

(RVAC and FODAVA)– December 2008, Atlanta, Georgia

Visual Analytics Digital Library(http://vadl.cc.gatech.edu)

• Developed by Georgia Tech (Foley et al.) in Southeast Regional Visual Analytics Center

• Repository for curriculum and education materials– Lecture notes– Homeworks, projects– Reference materials, videos, etc.

• Includes evolving taxonomy for Data and Visual Analytics• FODAVA will build upon this resource to

– Provide a library and web portal of FODAVA educational materials– Expand support to DAVA community to include FODAVA areas– Document curriculum develop efforts

Distinguished Lecture Series• Goal: Provide forum for

leaders in DAVA community to articulate vision and DAVA-related research and education activities and applications

• Plans (2009)– Lecture series featuring leaders in the data and visual

analytics community– Develop in collaboration with FODAVA partners, NVAC,

RVAC, DHS/S&T CoE– Webcast

Photo: Joe Kielman, VAC Consortium meeting, 2008

Outreach to Underrepresented GroupsExample: GT CRUISE Program

• CRUISE: CSE Research Undergraduate Intern Summer Experience

• Encourage students to consider PhD studies

• Diverse student participation

– Multicultural, emphasizing minorities, women

– U.S. and international students

• Ten week summer research projects in areas such as data and visual analytics, high performance computing, modeling & simulation

• Interdisciplinary individual and group projects

– Year-long collaboration with North Carolina A&T University

• CRUISE-wide events

– Weekly seminars (technical, grad studies)

– Social events

– Symposium: conference-style presentations

FODAVA Website http://fodava.gatech.edu

• Functionality– Dissemination of results to user communities– DAVA community events and meeting information

depot– Repository of data sets for FODAVA community

• Forum for FODAVA Community

• Maintain close collaboration with NVAC

FODAVA Annual Workshop(from Fall 2009)

• Annual Theme – Initially more mathematically/computationally oriented

– Increasing emphasis over time on visualization, human-computer interaction, cognitive science, …

• Organizers – Co-organized in collaboration among FODAVA-Lead,

FODAVA-Partners, NVAC, and DHS S&T Center of Excellence

• Time– Co-locate with NVAC Fall Consortium meeting

• Location– PNNL/NVAC, Richland, WA

FODAVA Annual Workshop 2009

• Theme: Machine Learning & Geometric Computing in Visual Analytics

• Organizers: Vladimir Koltchinskii (GATech)

and Mauro Maggioni (Duke)

• Time: November, 2009

• Location: PNNL/NVAC, Richland, WA

VAC Consortium Meetings

• Provides broader exposure of work, to DHS and NVAC communities

• Semi-annual:

Next Meeting: Nov 11-13, 2008, PNNL– Nov. 11: University Technical Exchange Day– FODAVA Panel session– FODAVA Demo/Poster session

• Please participate!

Additional Workshops

• FODAVA workshops at major conferences and meetings• IEEE VAST Conference

– Birds of a Feather session at VAST Oct., 2008

• Workshop on Temporal Analytics

• Other Potential venues– International Conference on Machine Learning– Neural Information Processing Systems (NIPS)– SIAM CSE / SIAM Optimization / SIAM ALA Conferences– ACM Knowledge Discovery and Data Mining (KDD)– AAAS meeting– Others?

Calendar of Events• Sept 2008: FODAVA Kick-Off Meeting• Oct 2008: VAST 2008 BoF Session• Nov 2008: VAC Consortium meeting, FODAVA

Panel and Poster/Demo Session• Dec 2008: DAVA Curriculum Workshop• May 2009: VAC Consortium Meeting• Oct 2009: VAST Conference• Nov 2009: VAC Consortium and FODAVA Annual

Workshop • Temporal Analytics Workshop under consideration

Project Materials• Goal: Articulate contributions being made by

the FODAVA community• Benefits

– Potential collaborators– Foster technology transition opportunities– Broader exposure to potential sponsors

• Materials requested– Project brochures and other collateral material– Videos especially welcome

• Tell us what you’re doing!• POC: Richard Fujimoto

Concluding Remarks• DAVA represents a new, exciting discipline that

brings together diverse communities• Research is motivated and driven by real-world

problems• FODAVA will play a key role in developing and

defining the foundations for DAVA• Communication and collaboration with other

elements of DAVA (e.g., NVAC, RVAC, DHS/S&T CoE) is essential– We need to educate ourselves!

Thank you!

Extra slides

Student Interns

• Support deep research collaboration between FODAVA lead, FODAVA partners, and PNNL / NVAC– Fundamental research driven by real-world

applications

• Leverage existing intern programs at PNNL– Summer interns

• Leverage GT distance learning capability for academic year interns

• Details to be determined

Undergraduate Education• Georgia Tech Threads curriculum

– Undergraduate program defined as a set of 8 threads– Thread is a body of coursework targeting a certain career

path, e.g., modeling and simulation, human computer interaction, embedded systems, etc.

– Students take two threads to complete BS in CS degree• Existing threads

– Modeling and Simulation: representing processes/systems– Devices: embedded computing– Theory: theoretical foundations of computing– Information Networks: information communication– Intelligence: human-level intelligence– Media: systems for creative expression– People: human-centric computing – Platforms: computing systems, architecture, languages

Modeling & Simulation Thread

• Many students come to Georgia Tech with an inherent love for math and science

• Computation provides a framework to view, understand, analyze, and design systems

Computational modeling is about going from

to

Fluid flow

model

Cellular Automat

a

Queueing Model

Involves developing mathematical / conceptual abstractions of systems that can be represented by efficient software

A Data and Visual Analytics Thread?

Foundations

ComputingMath Science

Discrete MathContinuous Math

TheorySoftwareHardwareAlgorithms

PhysicsBiologyChemistry

Computational Methodsfor Data AnalysisAnd Visualization

Application Discipline(pick one)

AeroCivil, Elect.

EAS, BiologyChemistry, Math

Physics, Industrial Eng.

?

• Curriculum• Foundational mathematics, computing, science• Data analytics, information visualization• Application-oriented specialization

• Integrated approach with capstone design project• Natural complement to modeling and simulation thread

Application Domains

• DHS: Intelligence analysis, Law Enforcement, Emergency response, Intrusion and fraud detection, ….

• BioMedical Informatics• Bioinformatics/Systems Biology• Astronomy• Text Analysis: Documents, e-mails, …• Cybersecurity• Transportation• …

Vladimir Koltchinskii, School of Mathematics

Sparse Recovery : For automatic determination of relevant features (Basis pursuit, Soft threshholding, LASSO …)Comprehensive theory is only starting to be developed

Penalized Empirical Risk Minimization: Basis for many solutions in basic problems of learning theory, e.g. regression, classification, density estimation

Challenge: extend the theory of sparse recovery to broader framework of learning theory, e.g. infinite classes of functions

• Machine Learning- Learning Theory- Feature Selection - Theory of Sparse Recovery - Empirical Risk Minimization

• Computational Statistics

• Continuous Optimization

- Interior-point methods- Semidefinite programming- Cone programming- Algorithms for large-scale optimization

• Computational Statistics and Graph Theory

Renato Monteiro, School of Industrial & Sys. Eng.

Dimension Reduction and Semi-definite Programming

• Higher level of reduction with more difficult objective function• Learning manifolds which preserve ordering of distances• Off-the-shelf SDP software does not scale• Design of efficient algorithms based on the first-order method, convex-concave saddle point problem

Alexander Gray, Computational Sci. & Eng.

Goal: make machine learning efficient– For massive datasets, e.g. for astronomy,

Large Hadron Collider, network traffic– For fast visualization, e.g. our new

manifold learning methods

• Developed fastest practical algorithms for many learning methods

• Coming in Dec 2008: MLPACK library

John Stasko, School of Interactive Computing and GVU Center

Visualization for Investigative Analysis - Putting the Pieces Together with Jigsaw

Information VisualizationHuman Computer Interaction

Help investigative analysts discover plans, plots and threats embedded across large document collections

Multiple visualizations (views) of the documents, entities, & their connections Views are highly interactive and coordinatedAnalysts explore the documents and entities through the views

Building a collaborative versionRepresenting reliability and uncertaintyEntity aliasing and hierarchy supportVisualizing the investigative process

Haesun Park, Computational Sci. & Eng.

Effective Dimension Reduction with Prior Knowledge

• Dimension Reduction for Clustered Data: Linear Discriminant Analysis (LDA), Generalized LDA (LDA/GSVD), Orthogonal Centroid Method (OCM)

• Dimension Reduction for Nonnegative Data: Nonnegative Matrix Factorization (NMF)

• Applications: Text Classification, Face Recognition, Fingerprint Classification, Gene Clustering in Microarray Analysis …

• Numerical Computing• Algorithms for Massive Data Analysis

- Dimension Reduction- Clustering and Classification

• Bioinformatics- Microarray analysis- Protein structure prediction

Education and Outreach Goals

FODAVA lead will• Encourage and coordinate development of

FODAVA Curriculum• Encourage and coordinate knowledge exchange

toward creating a workforce pipeline– Undergraduate education– Graduate education– Lifelong learning

• Facilitate research collaboration• Facilitate outreach to underrepresented groups

Engaging FODAVA Community

• FODAVA program provides a platform to bring together community of researchers, educators and practitioners

• Activities might include– Education workshops to share experiences,

develop best practices– Curriculum development– Repository of information and teaching

materials (e.g., SRVAC, VADL)