Lixing SongPhD Dissertation Defense

June 25, 2018 2:30 pm 258 Fitzpatrick Adviser: Dr. Aaron Striegel

Committee members:

Dr. Richard Billo Dr. Christian Poellabauer

Dr. Dong Wang

Title:

FAST MOBILE NETWORK CHARACTERIZATION: DESIGN, IMPLEMENTATION AND EVALUATION

Abstract:

The Internet today is actively embracing the evolution of mobilization. In 2017, mobile devices consumed 68% of overall Internet traffic. This number is expected to grow sevenfold in the next five years. Due to the limited resources on wireless spectrum, the relentlessly growing mobile data demand can portend an ominous future for the Quality of Experience (QoE) on mobile networks (e.g., WiFi and cellular). In addition, with the dynamics of the wireless channels the performance on mobile networks tends to vary significantly often making the wireless link as the bottleneck to decide QoE. Network characterization is a tool to understand the performance of mobile networks. Unfortunately, existing solutions are either cumbersome or inaccurate. Some are effective but the cost is prohibitively expensive (e.g., cost tens of megabyte data and take the order of tens of seconds to finish). Other methods are lightweight but yield low accuracy. Critically, the different transmission schemes under modern mobile networks make conventional characterization methods fail due to the issues with the lower-layer behaviors. Therefore, the key challenge is how to conduct mobile network characterization in an accurate and efficient manner.

Read More at: cse.nd.edu/Events

Success!

Keith FeldmanPhD Dissertation Defense

June 26, 2018 9:00 am 315 StinsonAdviser: Dr. Nitesh Chawla

Committee members: Dr. Ron Metoyer Dr. Christian Poellabauer

Dr. Annie Rohan

Title:

Beyond Modeling: The Emergent Role of Informatics in Advancing Healthcare Knowledge

Abstract:

Throughout the history of modern medicine, the observation of patient characteristics, health, and treatment has been driven by a desire to advance knowledge around human health and in turn better inform the practice and administration of healthcare. Captured through both practice and research such observations have provided the foundation on which evidence-based medicine was built. However, the advent of digitized health and wellness data has resulted in a volume of information so large it can no longer be reasonably expected that an individual can consume it. In response healthcare has turned to analytical approaches, combining statistics, machine learning, and computer science methodologies, to foster the growth of a field known as health informatics.

Drawing on data collected from entities around the globe, health informatics has successfully delivered the capability to identify and extract "best practices" amid the breadth of data available. Yet these practices alone have often been insufficient in advancing healthcare knowledge, as the ability to apply such information fulfills only one objective of learning as famously defined by Benjamin Bloom. This dissertation will present the notion that advancing informatics techniques now provides an opportunity to advance practitioner knowledge by providing evidence in a manner that realizes the broader set of Bloom learning objectives from understanding to synthesis.

Read More at: cse.nd.edu/Events

success!

aastha NigamPhD Dissertation Defense

June 25, 2018 10:00 am 384 NieuwlandAdviser: Dr. Nitesh Chawla

Committee Members: Dr. Omar Lizardo Dr. Ronald

Metoyer

Dr. Dong Wang

Title:

Beyond Who and What: Data Driven Approaches for User Behavior Modeling

Abstract:

Human behavior is highly complex and dependent on the interplay of various intrinsic and extrinsic variables making its study extremely challenging. In the era of big data, exponential growth of data across multiple domains has provided a unique opportunity for us to study human behavior, actions and relationships at scale by combining data from diverse data. This dissertation is guided by the following principles: 1) leveraging rich situational and interaction context 2) fusing information from heterogeneous data sources using efficient and effective computational models and 3) studying human behavior across multiple application domains to draw actionable insights to address real world challenges. To that end, the goal of this dissertation is to design data driven, context aware and user centered computational models that help us to understand, analyze, model and infer a range of individual and collective human behavior aspects from preference to engagement; from personality traits to political behaviors; from public opinion to relationship formation.

Read More at: cse.nd.edu/Events

Success!Ali Shahbazi

PhD DefenseJuly 1, 2018 3:00 pm 315 Stinson

Adviser: Dr. Walter ScheirerCommittee Members:

Dr. Patrick Flynn Dr. Adam Czajka Dr. Bobby Kasthuri

Title:

Computer Vision-based Approaches to Neural Circuit Tracing at Scale

Abstract:

Imaging is a dominant strategy for data collection in neuroscience, yielding 3D stacks of images that can scale to petabytes of data for a single experiment. Machine learning-based algorithms from the computer vision domain can serve as a pair of virtual eyes that tirelessly processes these images to automatically construct more complete and realistic circuits. In practice, such algorithms are often too error-prone and computationally expensive to be immediately useful. Therefore we introduce a new fast and flexible learning-free automated method for sparse segmentation and 2D/3D reconstruction of brain micro-structures.

Unlike supervised learning methods, our pipeline exploits structure-specific contextual clues and requires no extensive pre-training. This approach generalizes across different modalities and sample targets, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (µCT) of large tissue volumes. Experiments on newly published and novel mouse datasets demonstrate the high biological fidelity and recall of the pipeline, as well as reconstructions of sufficient quality for preliminary biological study. Compared to existing supervised methods, it is both Ali Shahbazi significantly faster (up to several orders of magnitude) and produces high-quality reconstructions that are robust to noise and artifacts.

Lin Yang

Success!

PhD DefenseJuly 2, 2018 3:00 pm 117J Cushing

Adviser: Dr. Danny ChenCommittee Members:

Dr. Sharon Hu Dr. Walter Scheirer Dr. Siyuan Zhang

Title:

New Approaches for Image Segmentation, Enhancement, and Analysis in 3D Brain Images

Abstract:

With the recent advances of optical tissue clearing technology, current imaging modalities are able to image whole mouse brain in 3D with single-cell resolution. This capability facilitates many exciting studies, such as researches on brain tumor metastasis and brain immune systems. In our preliminary research work, we have developed new methods to enhance, segment, and analyze 3D fluorescence microscopy images of mouse brain. By extracting quantitative information from such 3D images, many biological questions can be answered and many biological hypotheses can be generated and tested.

Due to the staining issue and the light-scattering issue, 3D images usually contain severe background noise. This background noise remains a significant obstacle for visualization and segmentation of these high-resolution 3D images. Thus, in the first step, We developed a new method that combines one-class learning and spatial filtering to remove background noise both accurately and fast.

To achieve quantitative analysis, we need to segment objects (e.g., cells, tumors, and blood vessels) in these images. Although deep learning based pixel-level segmentation has been well studied in computer vision area, 3D instance-level segmentation remains a challenge. Thus, in the second step, we developed a new method based on fully convolutional networks and k-terminal cut to achieve 3D instance-level cell segmentation.

Success!Shenglong Zhu

Oral Candidacy ExamJuly 6, 2018 2:00 pm 258 Fitzpatrick

Advisers: Dr. Chen /EmrichCommittee Members:

Dr. Greg Madey Dr. Meng Jiang Dr. Taeho Jung

Title:

Assembly Consolidation and Prediction of Large-Scale Genome Structure

Abstract:

Genome sequencing platforms have become essential to many sub fields of modern biology. Although large-scale and inexpensive sequencing data have been available for over a decade, a typical instance, also called a read, is short and often can not resolve more complex regions. More recently, so-called ``third generation'' sequencing has overcome previous length limitations by generating reads that can average tens of thousands of nucleotides. One of the more prominent successes of these emerging platforms has been using long reads to better assemble genomes, which has boosted downstream genomic analysis. Despite their appealing advantage in length, however, third-gen platforms have an extraordinarily high sequencing error rate. To begin mitigating this substantial limitation and provide new utility, our first aim in this proposal is to use long reads to detect and correct large-scale structural errors in draft assemblies. As in most genome assembly tasks, our first aim is based on a simple assumption that the genomes are haploids, which is not quite true in practice since a myriad of eukaryotes in nature are diploids or polyploids. To resolve this issue, we next propose to practice haplotyping on the sequences based on trio genome sequences where the source of homologous chromosomes can be deduced. Long reads themselves also have the advantages to improve downstream analysis.

Success!Andrey Kuehlkamp

PhD Defense

August 6, 2018 10:00 am 356A FitzpatrickAdviser: Dr. Kevin Bowyer

Committee Members:Dr. Adam Czajka Dr. Patrick Flynn Dr. Walter Scheirer

Title:“Advancing Iris Recognition through Search Performance, Presentation Attack Detection and Soft Biometrics”

Abstract:

The present study aims to contribute to three aspects in iris recognition technology. First, in the area of database search, an alternative search technique that has been used in operational 1-to-many matching scenarios was evaluated. Results show that critical degradation of the error rates can occur when compared to the traditional approach. Further evaluation showed, however, that upon careful selection of operational parameters it is possible to obtain reduction in the number of comparisons performed, with minimal loss in accuracy. Iris presentation attack detection (PAD) is the second area approached by this work. Recent works show that the ability to identify artifacts in cross-domain scenarios is still limited. This work proposes a new method for PAD that is based on CNN classification of multiple views of the data, followed by fusion of the multiple results. Evaluation shows the new approach outperforms the current state of the art in cross-domain PAD. Finally, gender prediction based on iris images is the last domain explored. Our study shows that despite an optimistic trend in related works, the actual potential of the iris texture for gender discrimination is very limited, and most of the information that is used in the process comes from surrounding regions.

Success!Afzal Hossain

Oral Candidacy

August 16, 2018 2:00 pm 258 FitzpatrickAdviser: Dr. Christian Poellabauer

Committee members:Dr. David Hachen Dr. Gregory Madey

Dr. Aaron Striegel

Title:

“Context-driven and Resource-Efficient Crowdsensing”

Abstract:

Mobile crowdsensing is the technique to extract or infer information about a person or group from smartphone or wearable data collected in opportunistic or participatory manner. This technique is widely used in psychology, social science, and mHealth research studies. Energy is a primary problem in smartphone sensing systems. Therefore, an efficient sensing system needs to maintain reasonable data quality while being resource conscious. Context-awareness plays the critical role in balancing the trade-off between data quality and energy. Moreover, it is very critical to activate the participatory sensing tasks at the right moment (i.e., during the right contexts) to ensure that the data quality is high and the resulting conclusions are meaningful.

However, context-awareness in a system can also lead to increased energy consumption. Therefore, a research challenge is to design a balanced system that is context-aware, energy-aware, while at the same time providing sufficient quality data for analysis. The proposed thesis will investigate techniques to address these challenges. The work will result in the design and implementation of a flexible crowdsensing platform where both opportunistic and participatory sensing activities can be configured in a context-driven way. To do so, it will rely on an energy efficient context engine that accumulates sensor data to provide various contexts to the client system. It will also provide a framework to schedule sensing tasks using complex contexts.

Success!Yue Ma

PhD DefenseNovember 9, 2018 8:30 am 165 Fitzpatrick

Adviser: Dr. Sharon HuCommittee Members:

Dr. Robert Dick Dr. Michael Niemier Dr. Yiyu

Shi Title:

“Improving Reliability of Real-Time Embedded Systems”

Abstract:

Multi-processing system-on-chips (MPSoCs) provide high performance and power efficiency. They have been widely used in many real-time applications such as automotive electronics, industrial automation, and avionics. Most of these applications must satisfy explicit deterministic or probabilistic timing constraints.However, due to CMOS technology scaling, multicore chips increasingly have higher power density and temperature, which, in turn, weakens the reliability and reduces system lifetime. Meanwhile, the decreased feature size of transistors and low core voltage and frequency design make the circuit more vulnerable to transient faults and degrade soft-error reliability. A systematic research to maintain the quality of service, improve lifetime reliability and soft-error reliability, and satisfy the real-time requirement becomes a major design concern in current computer systems, especially for embedded systems deployed in harsh environments.

We focus on MPSoCs with homogeneous and/or heterogeneous CPU cores, and/or integrated with GPU. We first develop an on-line framework to maximize lifetime reliability for MPSoCs through reliability-aware utilization control. Then, considering that the soft error is transient, we present a dynamic recovery allocation technique that if the remaining slack is adequate, any failed task can be recovered by executing again. Based on this technique, we propose two scheduling algorithms for task sets with different characteristics to improve system-level soft-error reliability. We focus on the ``big--little'' type MPSoCs in the third work. We introduce an on-line heuristic to maximize soft-error reliability under the peak temperature, power consumption, lifetime reliability, and real-time constraints. MPSoCs consisting of integrated CPU and GPU are desirable platforms for real-time embedded applications requiring massively parallel processing capability. Hence, in the last work, we aim at improving soft-error reliability for both CPU and GPU while satisfying the peak temperature, lifetime reliability, and real-time constraints. We have evaluated all our works on both Nvidia's TK1 and/or TX2 chip with tasks from multiple benchmark suites. The experimental results show that our works can improve lifetime reliability and/or soft-error reliability and satisfy the real-time constraint.

Success!

Pei LiOral Candidacy

October 30, 2018 10:00 am 315 Stinson

Adviser:Dr. Patrick Flynn

Committee members: Dr. Kevin Bowyer Dr. Domingo

Mery Dr. Walter Scheirer

Title:

“STUDYING UNCONSTRAINED DEGRADED FACE RECOGNITION WITH APPLICATIONS IN REAL SURVEILLANCE ENVIRONMENT”

Abstract:

Although face recognition systems have achieved impressive performance in recent years, the low-resolution face recognition (LRFR) task remains challenging, especially when the LR faces are captured under non-ideal conditions, as is common in surveillance-based applications. Faces captured in such conditions are often contaminated by blur, non-uniform lighting, and non-frontal face pose. In this proposal, I analyze face recognition techniques using data captured under low-quality conditions in the wild.

First, a high-quality dataset for person re-identification featuring faces is described. This dataset was collected from a real surveillance network in a municipal rapid transit system and includes the same people appearing in multiple sites at multiple times wearing different attire. Second, I provide a comprehensive analysis of experimental results for two of the most important applications (LR face re-identification and LR face identification) in real surveillance applications, and demonstrate practical approaches to handle both cases that show promising performance on some challenging datasets. Third, I propose a method to employ the large number of face image datasets at hands as well as exploiting unlabeled video data to train a reliable LR face recognition system.

Success!Aparna BharatiOral Candidacy

December 4, 2018 10:00 am 117I Cushing

Adviser: Dr. Kevin BowyerCommittee Members:

Dr. Patrick Flynn Dr. Nasir Memon Dr. Anderson Rocha

Dr. Walter ScheirerTitle:

Advanced Automated Image Forensics Solutions

Abstract:

Image Forensics has become an important area of research due to the exponential increase in availability and free exchange of media. The accessibility to good quality sensors and image hosting websites is now at the disposal of more users than ever. The increase in the exchange of images has also led to more ways to edit image content aimed towards achieving a certain goal. Whether it is altering one's portrait to look younger or adding external objects to pictures to change the perception and understanding of images, image editing software can allow a non-technical person to create images with changes undetectable to the naked eye. Even though most of the edits are benign, the methods can also be used to mislead readers or followers. In order to assess and regulate the quality of media, it is important to devise algorithms that detect and analyze manipulated content in an automated way. My work focuses on solving two such problems - retouching in face images and image provenance. The former detects manipulation in face images of individuals while the latter also requires retrieving the stages of evolution of the manipulated media object and the other objects contributing parts to the stages. The proposed methods used to solve the problems focus on efficiency and generalizability as the scale of operation is quite large and the problem is very unconstrained. Other contributions include formalizing the problem definition and creating solutions through techniques from image analysis, pattern recognition, and computer vision that are applicable to general cases of manipulated images on a large scale.

Success!Brian Page

Oral Candidacy

December 7, 2018 8:00 am 117I Cushing

Adviser: Dr. Peter KoggeCommittee Members:

Dr. Sharon Hu Dr. Christian Poellabauer Dr. Douglas ThainTitle:

Strong Scalability of Hyper-Sparse Problems

The ever increasing size of large data sets presents complex computational challenges for analysts and researchers alike. Nearly every field of engineering deals with problems resulting in the generation of sparse data sets, often in the form of sparse data types. Sparse data types posses irregular access patterns due to the data compression techniques intended to boost performance by eliminating the need to store superfluous data. While there has been much work focused on weak scaling of sparse problems, there has been relatively little research into the strong scaling of sparse problems. While both weak and strong scaling have advantages and disadvantages, as data sets continue to increase the need for efficient strong scaling of sparse problems is rapidly becoming apparent.

We intend to investigate the performance characteristics of sparse problems in an effort to identify elements of implementation which impact performance, as well as to what degree they do so. First, I propose the development of separate hybrid SpMV codes targets to GPUs and the Emu migrating thread architecture. Second a hybrid bipartite matching application will be created using the vertex-centric HavoqGT framework. Third a code base of generalized sparse operators intended for strong will be developed and evaluated. Lastly a predictive hybrid sparse model will encompass the knowledge obtained during the preceding studies. This proposal seeks to broaden understanding into the strong scaling potential of irregular sparse problems, aiding in the push for exascale technologies.

SuccessCharles ZhengOral Candidacy

December 7, 2018 258 Fitzpatrick 1:00pm

Adviser: Dr. Douglas ThainCommittee members:

Dr. Christian Poellabauer Dr. Lukas Rupprecht

Dr. Dong WangTitle:

The Challenges of Scaling up Scientific Computing Workflow with Container Technology

Abstract:

With the fast evolved infrastructure and steadily dropping price of cloud computing, the interest in migrating high-throughput scientific workflow to the cloud is growing rapidly. Rather than be limited to the traditional HPC centers, scientific workflows can be run on commercial cloud that not only make use of new technology but also scale up the workflow to required degree. However, high-throughput workflows often have unique characteristics that might not fit into standard cloud environment.

The author propose that to scale up high-throughput workflows with new technologies or platforms, there is a set of rules need to be followed, More specifically, the author discusses the challenges met when trying to expand the scale of high-throughput workflows with container runtimes and container orchestrator, explain how to approach these challenges for adapting emerging technologies and platforms for high-throughput workflows.

SuccessJinglan Liu

Oral CandidacyJanuary 15, 2019 3:00 pm 315 Stinson

Adviser: Dr. Yiyu ShiCommittee:

Dr. Sharon Hu Dr. Michael Niemier Dr. Jinjun Xiong

Title:

Acceleration and Applications for Generative Adversarial Networks

Abstract:

In recent years, generative adversarial networks (GANs), which are spin-offs from conventional convolutional neural networks (CNNs), have attracted much attention in

the fields of reinforcement learning, unsupervised learning and also semi-supervised learning. To solve the high computational and memory cost problem inherited from CNN, compression techniques such as using binary weights instead of floating-point numbers can be readily applied to discriminator networks in GANs; however, adopting it directly will fail generator networks. We solved this problem by adaptive partial binarization.

In addition, I attempted to extend GANs into two promising applications for further speedup. I have successfully applied Multi-Cycle GANs to the CT (computed tomography) artifacts removal problem. It is always desired to find a approach that removes the artifacts in CT images to make them as clear as possible for doctors. Multi-Cycle GAN solved this task gradually. Upon similar training and testing time, Multi-Cycle GANs can achieve better CT scan image quality than vanilla CycleGANs, which is the state-of-the-art method in academia.

I propose to apply GAN into helping noise maps simulation. The relentless efforts towards power reduction of integrated circuits have led to the prevalence of near-threshold computing paradigms. As a result, designers seek to contain noise violations, commonly known as voltage emergencies, through various techniques. All these techniques require accurate capture of voltage emergencies through noise sensors, which are based on a large amount of circuit simulations. Nevertheless, circuit simulation is extremely time-consuming, and it usually costs several weeks to produce enough simulated samples for the noise sensor placement decision. I will investigate an approach based on GANs, which greatly reduced the time needed to generate plenty samples for a good noise sensor placement solution. 

Success

Antonios Anastasopoulos

Dissertation Defense

January 21, 2019 1:00 pm 315 Stinson

Adviser: Dr. David Chiang

Committee:

Dr. Graham Neubig Dr. Walter Scheirer

Dr. Tim Weninger

Title:Computational Tools for Endangered Language Documentation

Abstract:

The traditional method for documenting a language involves the collection of audio or video sources, which are then annotated at multiple granularity levels by trained linguists. This is a painstaking and time-consuming process, which could benefit from machine learning techniques at almost all stages. However, most existing machine learning methods are being developed for high-resource languages and rely on abundant data, rendering them unsuitable

for such applications.

At the same time, for many low-resource and endangered languages speech data is easier to obtain than textual data, particularly since most of the world's languages are unwritten. Nevertheless, it is relatively easy to provide written or spoken translations for audio sources, as speakers of a minority language are often bilingual and literate in a high-resource language.

This work is aimed at solving certain problems that arise in the documentation process of an endangered language, due to the minimal annotated resources that are available at this stage. This dissertation mainly focuses on spoken corpora of endangered and low-resource languages with limited translation annotations, tackling problems that cover every layer of linguistic annotation:

speech-to-translation alignment: we present an unsupervised method for discovering word or phrase boundaries in the audio signal and aligning the discovered segments with translation words.

speech transcription: we developed two novel neural methods for creating a phoneme or grapheme level transcription of the audio, also utilizing any available translations.

speech translation: our novel multitask neural model jointly produces a transcription and a (free) translation of an audio segment.

morphological analysis: producing a layer of annotation that provides word-level or morpheme-level information. In this work we focus on grammatical (part-of-speech) tagging on an endangered language.

Success

Nathaniel Kremer-Herman

Oral Candidacy ExamFebruary 8, 2019 10:00 am 315 Stinson

Adviser: Dr. Douglas thain

Committee Members:

Dr. Paul Brenner Dr. Peter Kogge Dr. Ron Metoyer

Title:

Troubleshooting Distributed Applications Using a Graph Representation

Abstract

Troubleshooting distributed applications is hard. It is not always feasible to expect a researcher to know the fine-grained details of how the different components of their application interact (whether individual units of work or persistent processes communicating across compute nodes). Because of this, research can grind to a halt when a critical component is borked. The researcher may have to sift through log files gathered from disparate compute nodes, and those log files may not accurately track all the necessary metadata to connect one component to another (i.e. environment variables, shared files, or flags/tokens). I propose two tools which make this troubleshooting task easier for a researcher. The first tool, a log parser, will create a unified record of events by creating a single log of state changes found across the debug logs of individual components of an application. The second tool, a querying utility, will allow the researcher to query their debug logs as a collective record of events which will show the researcher the history of why their queried component arrived at its final state. It will also show how that final state is passed on to other components if applicable. I anticipate this tool will demonstrate actionable troubleshooting information so the researcher can fix their application quicker and with less domain knowledge required than manual debug log analysis. I also posit this form of log analysis will provide a more exact result than the state of the art which relies upon statistical models to infer context upon unstructured log events with no domain knowledge.

Arturo Argueta

Dissertation Defense

February 27, 2019 12:00 pm 315 Stinson Remick

Adviser: Dr. David Chiang

Committee Members:

Dr. Adam Lopez Dr. Michael Niemier

Dr. Tim Weninger

Title:Accelerating Natural Language Processing algorithms using Graphics Processing Units

Abstract: Natural Language Processing (NLP) models the different techniques computers use to understand and interpret human languages. NLP covers a wide range of sub-topics such as syntax (analyzing if words in an utterance are well arranged), semantics (understanding the meaning of combined words), and discourse. Most state-of-the-art NLP systems feed large amounts of natural language text into different models for training and testing. One problem with natural language corpora is the unbalanced frequency of rare terms against commonly used words. The word-level frequency in natural language creates irregular sparsity patterns, and these patterns generate sparse data structures that do not perform well on parallel architectures. Asynchronous methods work best on specific sparse distributions. Ideally, the entire computation time should be spent on dense values only, and computation time on sparse elements should be minimized.

Graphics Processing Units (GPU) are widely used to process a large quantity of operations in parallel. A problem with the use of these accelerators is that not all computation problems can be parallelized, and not all parallel adaptations run faster than a serial CPU counterpart. Using GPUs to process sparse data structures of different sizes poses additional problems. A large part of the computation time will be spent on sparse regions if the parallel implementations do not take advantage of the partially dense properties of the input.

Significant speedups can be achieved if a parallel implementation is tailored to the sparsity pattern of the problem being solved and the targeted architecture. Our work adapts computational methods used in NLP to run efficiently on a parallel architecture using high-performance computing concepts. All contributions focus mainly on the GPU device designed to carry out a large amount of computations faster than several off-the-shelf CPU architectures. This dissertation covers our adaptations of sparse NLP algorithms to the GPU architecture. We carry out experiments using different GPU architectures and compare the performance on different dataset sizes. Our results demonstrate that GPU adaptations can significantly reduce the execution time of different sparse NLP algorithms.

Success!Tianchen Wang

Oral Candidacy Exam

February 28, 2019 7:00 pm 165 Fitzpatrick

Adviser: Dr. Yiyu S

Committee Members:

Dr. Danny Chen    Dr. Sharon Hu Dr. Jinjun Xiong

Title:

Statistical Neural Networks: Concepts, Frameworks and Applications

Abstract:

Deep convolutional neural networks (CNNs) have drastically improved the performance in various computer vision tasks. However, the accuracy improvement brings the challenges of deep CNNs implementations in the application of computer vision. The slow image(s) processing speed has difficulty in meeting the requirement of the fast-evolving imaging techniques. The considerable computation cost of inference has also increased rapidly with the ever-growing demands for higher performance. Meanwhile, the limitation exists in most CNN computer vision applications that they may not fully utilize the temporal and contextual correlation.

To bridge these gaps, in this proposal we propose to explicitly model the target spatio-temporal correlation for real-time fast and efficient inference as a general speedup technique. We achieve this by extracting parameterized canonical form distributions from correlated inputs (such as adjacent frames in a video snippet) and propagate the distributions forward and backward. Since we model the inputs as a parameterized statistical distribution, our network is called Statistical Convolutional Neural Network (SCNN). Then, we use the video object detection task as a verification example to prove the effectiveness of SCNN. To further improve the performance of SCNN and overcome the information loss during canonical form distributions extraction, we propose multiscale statistical dense net (multiscale SD-Net) and experiment with the task of 3D cardiac MRI segmentation. Based on these contributions we propose to extend SCNN to the application of video super-resolution as our future work.

Neil Butcher

Oral Candidacy

March 12, 2019 2:00 pm 165 Fitzpatrick

Adviser: Dr. Peter Kogge

Committee Members:

Dr. Siddharth Joshi Dr. Stephen Oliver

Dr. Douglas Thain

Developing Algorithmic Techniques to Utilize Multilevel Memory

Abstract:

The goal of this work is to develop algorithmic techniques that take advantage of MLM. To achieve this goal we start by examining previous techniques to similar problems. Then using these different algorithmic techniques. We apply these techniques to several different problems (sort, dense matrix multiply, wavelet transforms, Jaccard). We then demonstrate how different algorithmic techniques affect performance. Using tools and small kernels we observe insights into how problem characteristics affect the usefulness of the MLM. We focus on building solutions to many-core nodes that contain MLM in particular Intel's Knight's Landing (KNL). We focus on observations that also extend to future MLM systems. We differentiate behavior between problems based on the context of problem characteristics such as sparsity, memory bandwidth bounded, L1 cache bounded. Some problems are suited to one algorithmic technique. Other problems may perform better using other algorithmic techniques. We start by building an understanding of how problems characteristics affect the behavior of multilevel memory. Once we have this understanding, we intend to consider to design hardware to better take advantage of MLM.

Success!

Rachael Purta

Dissertation DefenseMarch 19, 2019        2:00 pm        258 Fitzpatrick

Adviser:  Dr. Aaron StriegelCommittee Members:

Dr. David Hachen      Dr. Ron Metoyer     

Dr. Christian Poellabauer

Title:

Characterizing Bluetooth Low Energy Beacons for Studying Social Behavior

Abstract:

Bluetooth low energy (BLE) beacons are a relatively new technology enabled by both the rise of cheaper sensors for the Internet of Things (IoT), and innovations in the Bluetooth standard to decrease energy cost. As a result, BLE beacons have continued to decline in price and can now last several years on readily available commercial batteries, allowing their use in a variety of applications such as indoor location, occupancy detection, building utilization, and others. Meanwhile, IoT innovations have also enabled an increase in available sensors on commercial smartphones, as well as fueled their incredible popularity, causing

not only technology researchers but those from medical, psychology, sociology, economics, and other areas to appreciate the value of smartphones for large-scale, unobtrusive, behavioral research. Of particular interest to many is the study of social behavior, which can consist of communication, co-location, face-to-face conversations, and more. My research lies in the center of these three movements, characterizing and using BLE beacons, in conjunction with the smartphones that detect them, for the large-scale study of social behavior.

In this work, I evaluate the use of BLE beacons for sensing social behavior in three ways. First, I will show that direct distance estimation from the received signal strength indicator (RSSI) of BLE, commonly used for classic Bluetooth in previous social sensing work, is difficult due to high signal variability at farther distances, especially when considering real-world sensing scenarios such as carrying the smartphone in a pocket. Second, I will present a new fingerprinting method for room-level indoor positioning that relies on which beacons are detected, not the RSSI reading, yet has high performance and robustness in real-world sensing situations on-par with or better than some traditional RSSI methods. Finally, I will discuss two case studies, room utilization and friendship prediction from dining visit habits alone, using data collected from a 40-beacon deployment in a campus dining hall.

Success!Nathan Blanchard

Dissertation DefenseMarch 18, 2019 10:45 am N 134 Duncan

Adviser: Dr. Walter ScheirerCommittee Members:

Dr. Kevin Bowyer Dr. Patrick FlynnDr. Christopher Forstall Dr. Sean Kelly

Title:

Quantifying Internal Representation for Use in Model Search

Abstract:

Machine learning models are expected to work across a variety of settings, or inputs, with reasonable performance. A model that does not exhibit reliability work across settings cannot be trusted for real-world usage. The measure of a model’s adaptability across inputs is its generalization, or generalizability. Traditionally, a machine learning model is evaluated for generalizability by testing it on unseen

data, unseen inputs. The justification behind this is models that are unable to adapt to this unseen data cannot be relied on to adapt to other unseen data. This evaluation considers metrics of correctness to assess performance on the unseen data, measuring the model’s ability with metrics like accuracy, precision/recall, specificity, and F1 Score. This style of evaluation effectively weeds out models which cannot hope to generalize to unseen inputs by testing the model on a sample of inputs, and showing the model fails. However, this is no guarantee; a model that succeeds on this set of unseen inputs may still fail on another. In this work, I show that generalization behavior can be inferred by evaluating models across an intentional range of inputs and studying how internal behavior varies across that input range, specifically in correspondence with the way instance similarity varies across the input range. The way an input progresses through a model’s internal processes can be thought of as the internal behavior of the model in reaction to that input. For neural networks, internal behavior is literally the activation patterns of the network’s neurons in response to the input stimuli. A neural model’s internal behavior between two inputs can be abstractly compared by correlating the activations in response to each instance. A measure of the model’s internal behavior is obtained by repeating this process across the full set of stimuli pairs. Consistent behavior manifests as similar behavior for similar inputs, and dissimilar behavior for dissimilar inputs. If a model’s internal behavior is inconsistent, or unpredictable, then it cannot be trusted to generalize to unseen input

Success!Xueying Wang

Master Thesis DefenseMarch 18, 2019 3:00 pm 165 Fitzpatrick

Adviser: Dr. Meng JiangCommittee Members:

Dr. David Chiang Dr. Taeho Jung Dr. Tim Weninger

Title:

Improving Information Extraction via Truth Finding with Data-Driven Commonsense

Abstract

The task of temporal slot filling (TSF) is to extract values of specific attributes for a given entity, called “facts”, as well as temporal tags of the facts, from text data. While existing works denote the temporal tags as single time slots, in this work, we introduce and study the task of Precise TSF (PTSF), that is to fill two precise temporal slots including the beginning and ending time points. Based on our observation from a news corpus, fewer than 0.1% have time expressions that contain the two points, and the articles' post time, though often available, is not as precise as the time expressions of being the time a fact was valid. Therefore, directly decomposing the time expressions or using an arbitrary post-time period cannot provide accurate results for PTSF. The challenge of PTSF lies in finding

precise time tags in noisy and incomplete temporal contexts in the text. To address the challenge, we propose one unsupervised approach based on the philosophy of truth finding. The approach has two modules that mutually enhance each other: one is a reliability estimator of fact extractors conditionally on the temporal contexts; the other is a fact trustworthiness estimator based on the extractor's reliability. Commonsense knowledge (e.g., one country has only one president at a specific time) was automatically generated from data and used for inferring false claims based on trustworthy facts. For the purpose of evaluation, we manually collect hundreds of temporal facts from Wikipedia as ground truth, including country's presidential terms and sport team's player career history. Experiments on a large news dataset demonstrate the accuracy and efficiency of our proposed algorithm. Furthermore, we explore the possibility on another truth finding approach, probabilistic graphical model based method, to solve the same problem.

Success!

Andrew WoodMaster Thesis Defense

March 19, 2019 1:00 pm 100 StinsonAdviser: Dr Collin McMillan

Committee members: Dr. David Chiang Dr. jane Cleland-Huang

Title:

"LOW DATA DIALOGUE ACT CLASSIFICATION FOR VIRTUAL AGENTS DURING DEBUGGING"

Abstract:

A “dialogue act” is a written or spoken action during a conversation. Dialogue acts are usually only a few words long, and are divided by researchers into a relatively small set (often less than 10) of dialogue act types, such as eliciting information, expressing an opinion, or making a greeting. Research interest into automatic classification of dialogue acts has grown recently due to the proliferation of Virtual Agents (VA) e.g. Siri, Cortana, Alexa. But unfortunately, the gains made into VA development in one domain are generally not applicable to other domains, since the composition of dialogue acts differs in different conversations. In this thesis, I target

the problem of dialogue act classification for a VA assistant to software engineering repairing bugs in a low data setting. A problem in the SE domain is that very little sample data exists. Therefore, I present a transfer learning approach to learn on a much larger data set for general business conversations, and apply the knowledge to a manually created corpus of debugging conversations collected from 3 0 professional developers in a “Wizard of Oz” experiment and manually annotated with a predetermined dialogue act set. In experiments, we observe between 8% and 20% improvements over two key baselines. Additionally, I present a separate dialogue act classifier on the manually collected data set that uses a manually discovered SE specific dialogue act set which achieves on average 69% precision and 50% recall over 5-fold cross validation.

Success!Justin DeBenedetto

Oral Candidacy Exam

March 22, 2019 2:00 pm 247C Fitzpatrick

Adviser: Dr. David Chiang

Committee members:

Dr. Daniel Gildea Dr. Tijana Milenkovic

Dr. Tim Weninger

Title:

Multiset and DAG Automata for Abstract Meaning Representation

Abstract:

Natural Language Processing (NLP) utilizes computers to process human language for various purposes. NLP applications affect people on a daily basis, from digital assistants to automated customer service to automatic translation. These systems take input primarily in the form of speech or text. When using these systems, however, we often may find that the systems are sensitive to the way we express our thoughts. For example, when using a search engine such as Google or Bing, if the results we get are not what we are looking for we may rephrase our search and get potentially very different results. Some of these may be attributed to ambiguity in our

input, but there exists an underlying problem which may be remedied by adding more semantic processing.Semantic graphs for NLP seek to encode the meaning of their input. By working from the semantics of a sentence, for example, when performing machine translation we may better preserve the meaning of the sentence. In this work we look specifically at Abstract Meaning Representation (AMR) as our semantic graph formalism. AMR allows many sentences to map to the same graph. This reflects the fact that we are able to formulate the same thought in many different ways. The tasks of converting between a string of text and an AMR graph are non-trivial.Throughout this proposal we build up the theory underlying some potential methods for tackling the problem of converting an input sentence into an AMR. These methods build upon existing work, utilizing in particular multiset automata and directed acyclic graph (DAG) processing algorithms. It is worth noting that while we use AMR as our motivation and testing formalism, our methods generalize to other semantic representations as well. Our proposed contributions include theoretical improvements to the time and space complexity of existing algorithms, novel training methods for learning from data, implementation of these systems, and combining them into a system which can produce an AMR from an input sentence.

Frederick Nwanganga

Dissertation Defense

March 22, 2019 12:00 pm 384 nieuwland

Adviser: Dr. Nitesh Chawla

Committee Members:

Dr. Michal Chapple Dr. Gregory Madey

Dr. Ron Metoyer

Title

"Optimizing Workload Resource Allocation in the Cloud: A Data-Driven Approach"

Abstract:

 As more organizations migrate their services from local data centers to public cloud infrastructure, the challenge of balancing the competing needs of adequate resource allocation and cost minimization while maintaining Quality-of-Service guarantees becomes critically important. As a result of the pay-as-you-go cloud economic model, organizations are now more likely to shift their infrastructure operations budget model from Capital Expenditure (CapEx) to Operational Expenditure (OpEx). This change invariably leads to increased cost visibility as well as increased demand for optimal resource utilization.

Most cloud migration efforts either rely on domain knowledge or employ a heuristic approach when allocating resources to workloads. While this approach may be sufficient for the initial lift, it is an inefficient strategy for ongoing data center operations in the cloud. It increases the risk of over-provisioning and under-provisioning which consequently result in increased Total Cost of Ownership and potential Service Level Agreement violations.

Read more at: CSE.ND.EDU/Events

Yuan Gong

Oral Candidacy Exam

March 25, 2019 10:00 am 165 Fitzpatrick

Adviser: Dr. Christian Poellabauer

Committee Members:

Dr. Adam Czajka Dr. Meng Jiang

Dr. Taeho Jung

Title:Novel Healthcare Applications and New Security Concerns of Speech Processing

Systems

Abstract:

Conventionally, most research on speech processing focuses on automatic speech recognition (ASR), i.e., transcribing speech to text. However, natural speech does not only contain text content information, but also much other information such as emotion and even the speaker's health status. That means we can extract more information from speech besides text content and use them for novel applications. Specifically, we

can develop speech processing systems for novel healthcare applications such as building convenient and low-cost diagnose, screening, or monitoring solutions. In the first part of the proposal, we propose to investigate how to build speech processing systems for novel healthcare applications.

On the other hand, with the fast-growing number of users and usage scenarios, the security problem of speech processing systems (e.g., Amazon Alexa) becomes a new concern. Recent work has found speech processing systems are vulnerable to multiple types of attacks such as replay attack, audio adversarial attack, and ultrasound attack. These attacks are demonstrated to be extremely threatening in the experimental settings, however, it is still unclear how dangerous they are in realistic settings. Therefore, in the second part of the proposal, we propose to identify the real-world security threats to modern speech processing systems, and designing an effective defense strategy.

Overall, the proposed research aims to address two orthogonal problems about speech processing, and the goal of the research is to broaden the applications and proves the robustness of machine learning based speech processing systems.

Maria Glenski

Dissertation Defense

March 26, 2019 2:00 pm 315 Stinson

Adviser: Dr. Tim Weninger

Committee Members:

Dr. meng Jiang Dr. Huan Liu Dr. Dong Wang

Title:Social News Consumption in Systems with Crowd-Sourced Curation

Abstract:

People frequently supplement or have replaced their consumption of news from traditional print, radio, or television news sources with social news consumption from online social media platforms such as Facebook, Twitter, or Reddit. Reliance on social media sites as primary sources of news and information continues to grow and shows little sign of decreasing in the future. Tasked with curating an ever-increasing amount

of content, providers leverage user interaction feedback to make decisions about which content to display, highlight, and hide. The sheer volume of new information being produced and consumed only increases the reliance that individuals place on anonymous others to curate and sort the massive amounts of information. Here, I describe several analyses and predictive models of user-behavior in social news platforms such as: user-interactions that rely on or influence the aggregate, anonymous crowd-ratings used to identify news-worthy content and user-interactions with news sources of varied credibility in particular. The central focus of this work is to understand not only how individuals consume social news, but also how they contribute to the spread and reception of credible news and misinformation. Experimental results and predictive models demonstrate the influence of algorithmic biases on social news consumption patterns and the distinctions in the consumption of, response to, and propagation of information from news sources of varied credibility.

Success!Louis Faust

Oral Candidacy Exam

March 29, 2019 9:00 am 315 Stinson

Adviser: Dr. Nitesh Chawla

Committee Members:

Dr. keith Feldman Dr. David Hachen

Dr. Christian Poellabauer

Title:

Modeling Physiological & Behavioral Data Streams Towards Health Insights"

Abstract:

Motivated by the evolution of wearable technology, the quantified-self (QS) movement has become a mainstream phenomenon, generating data at an unprecedented rate. QS is the utilization of technology to acquire data from an individual's everyday life with the goal of enhancing one's self-knowledge. Commonly acquired measures have included heart rate and step counts, with popular devices such as Fitbit and Apple Watch collecting these data in a continuous and unobtrusive manner.

Given their emphasis on capturing health behaviors, personal tracking devices stand to serve as invaluable tools for the science of health and wellness. Through highly granular physiological and behavioral measurements, these devices can provide a rich history of one's health as opposed to a single snapshot. As such, they have become the cornerstone to observational and interventional research in personal health. Despite integration into these fields, these devices currently host a deficit towards informing the user of their own health. Device interfaces often serve only as a digital mirror: offering the user a rich summary of their collected data, while providing no analysis or actionable insights to draw from.

Success!Chuxu Zhang

Oral Candidacy ExamMarch 21, 2019      10:00 am      384 Nieuwland

Adviser:  Dr. Nitesh ChawlaCommittee Members:

      Dr. David Chiang      Dr. Meng Jiang      Dr. Xiangliang Zhang

Title:

Relational Representation Learning for Heterogeneous Graphs

Abstract:

Online web services are fast developing. Most of these real-world complex systems can be structured into heterogeneous graphs that encode rich information through multi-typed nodes, relationships, as well as contents associated with nodes. Heterogeneous graph mining has gained a lot of attention in recent years. The labor-consuming feature engineering activity, however, responds to the requirements of the heterogeneous graph mining tasks, requiring both a domain understanding and large exploratory search space

for possible features. Therefore, generalizing the feature engineering activity through representation learning that automates the discovery of useful relational latent features among nodes and links poses critical challenges yet advances the research area. Moreover, a generalized feature representation from such heterogeneous graphs can lend itself to a variety of graph mining tasks (such as link prediction, node classification, etc.), mitigating the need for a purposeful feature engineering dependent on the task at hand.

In this proposal, I propose a series of relational representation learning methodologies to facilitate the generalized feature engineering and its application to a variety of tasks for validation as well as application. The core contributions of my dissertation include: algorithms for personalized ranking in heterogeneous graphs; heterogeneous graph embedding techniques; learning to learn frameworks of relational representation in heterogeneous graphs; and several use-case applications for graph mining. These are validated on a number of real-world data sets demonstrating the accuracy as well as generalization of the proposed work.

Success!Kangkang Li

Dissertation DefenseMarch 29, 2019      2:00 pm      165 Fitzpatrick

Adviser:  Dr. Jarek Nabrzyski

Committee Members:

Dr. Gregory Madey      Dr. Maciej Malawski     

Dr. Scott Nestler

Title:

“Topology-Aware Job Scheduling and Placement in High Performance Computing and Edge Computing Systems”

Abstract

The interconnection topology of the computing nodes in a distributed system plays an important role in the way that the jobs should be scheduled and allocated. In this work, I address two groups of resource allocation problems. The first group of problems is the topology-aware job scheduling and placement problems in high performance computing (HPC) systems, where a 3D torus-based interconnection topology is used in the system. The second group of problems is the virtual machine (VM) placement

and migration problems in the edge cloud system, in which a two-layer star topology is applied in the considered edge cloud architecture.

In the first group of problems, i.e. HPC resource allocation problems, I address the topology-aware job scheduling and placement problems in a 3D torus-based HPC system, with the objective of reducing system fragmentation and improving system utilization. Firstly, for the job scheduling problem, I propose a packing-based job scheduling strategy, which reduces the external fragmentation caused by using the First Come First Served (FCFS) + backfilling strategy. Secondly, I study the first case of the job placement problem, where each job is allocated a convex prism shape. I propose a job placement algorithm based on a local migration and a global migration process, which aims at reducing the internal and external fragmentation in the job placement process.

Success!

Chao Huang

Dissertation Defense

March 28, 2019 9:00 am 384 NieuwlandAdviser: Dr. Nitesh Chawla

Committee Members:

Dr. Meng Jiang Dr. Taeho Jung

Dr. Pablo RoblesGranda

Title:

Spatial-Temporal Data Inference and Forecasting: Models and Applications

Abstract:

With the advances in wireless and location-acquisition communication technologies, spatial-temporal data is ubiquitous in real-world ranging from social media to urban planning. Two important tasks in the spatial-temporal analysis are (i) inference, e.g., estimating the data for unknown locations by taking advantage of the observations from known locations; (ii) forecasting, e.g., with the aim of predicting future trends by understanding past observations with spatial-temporal information.

A key challenge in mining spatial-temporal data often lies in the complex dependence structures from spatial-temporal dimensions. To fully harness the power of spatial-temporal data, this work aims to develop novel machine learning frameworks to make inferences and predictions on data by uncovering the dynamic spatial-temporal patterns. Work in this thesis investigates various applications that help data-driven decision makers by providing a better understanding of our physical environments. The results of the work in this proposal are important because they provide a solid analytical foundation to accurate and effective modeling of spatial-temporal data, and directly contribute to the emerging field of computational sustainability, social science, and urban planning.

Success!

Poorna Talkad Sukumar

Oral Candidacy Exam

April 5, 2019      2:00 pm       315 Stinson

Adviser: Dr. Ronald Metoyer

Committee Members:

Dr. Gregory Madey   Dr. Lace Padilla    Dr. Aaron Striegel

Title:

“A Human-Centered Approach for Identifying Potential Cognitive Biases in Decision-Making Domains and Designing Visualization Techniques for Their Mitigation”

Abstract:

Cognitive biases have been studied extensively and are known to occur in various domains and also in several everyday-scenarios of decision making. While not all of their occurrences have significant consequences, these biases can play a significant and alarming role in certain consequential decision-making domains, such as college admissions, clinical diagnoses, and intelligence analysis, where the judgments made predominantly depend on expertise and subjective evaluations. In this dissertation work, we began by studying one such domain—the holistic review process in undergraduate college admissions in the United States, by means of situated interviews and observations. We employed cognitive task analysis (CTA) and a socio-organizational approach to characterize the review process at a highly-selective, private university and identify potential cognitive biases the reviewers may be subject to when reviewing applications.

Based on the findings from our domain characterization, we then turned our focus to supporting the decision-making process using visualizations to both enable the

reviewer tasks, as well as address potential reviewer biases in the process. We present numerous strategies for mitigating the identified reviewer biases by developing a theoretical understanding of the cognitive bias manifestations as well as based on literature on visualization tools and techniques used to aid sense making processes. For the final phase of the dissertation, we propose the design of bias-mitigating visualization techniques and their evaluation using quantitative empirical studies. We focus on one particular cognitive bias—anchoring, which refers to the bias in judgments resulting from the influence of some previously encountered information, and conduct experiments to study its effects in visualizations for simple, domain-independent tasks.

Success!Brandon RichardWebster

Oral Candidacy Exam

April 16, 2019      7:00 pm      315 Stinson

Adviser:  Dr. Walter Scheirer

Committee Members:

Dr. Adam Czajka      Dr. Patrick Flynn     

Dr. Michael Milford

Title:

"Visual Psychophysics For Computer Vision"

Abstract:

By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition. But even in light of breakthrough results on recent benchmarks, it is still fair to ask if our recognition algorithms are doing as well as we think they are. The vision sciences at large make use of a very different evaluation regime known as Visual Psychophysics to study visual perception. Psychophysics is the quantitative examination of the relationships between controlled stimuli and the behavioral responses they elicit in experimental test subjects. Instead of using summary statistics to gauge performance, psychophysics directs us to construct item-response curves made up of individual stimulus responses to find perceptual

thresholds, thus allowing one to identify the exact point at which a subject can no longer reliably recognize the stimulus class.

In this work I will demonstrate the efficacy of visual psychophysics for computer vision from four main perspectives: visual psychophysics for evaluation, visual psychophysics for explainability, visual psychophysics for improved generalizability, and finally visual psychophysics for a real life application. The arch from evaluation to application completes a life cycle of a computer vision algorithm, providing positive evidence to the community that visual psychophysics can aid any stage of development.

Success!Sudip Vhaduri

Dissertation DefenseApril 11, 2019      8:30 am      258 Fitzpatrick

Adviser:  Dr. Christian PoellabauerCommittee Members:

Dr. David Hachen      Dr. Aaron Striegel      Dr. Dong Wang

Title:

"Reliable Discovery of Places of Importance and Behavioral Patterns from Mobile Crowdsensed Datasets"

Abstract

It is becoming increasingly important to accurately detect a user's presence at certain locations during certain times of the day, e.g., to study the user's patterns with respect to mobility, behavior, or social interactions and to enable the delivery of targeted services. However, instead of geographic locations, it is often more important to determine a locale that is relevant to the user, e.g., the place of work, home, homes of family and friends, social gathering places, etc. These significant personal places can be determined through analysis, e.g., via segmentation of location traces into a discrete sequence of places. However, segmentation of traces with many gaps (e.g., due to loss of network connectivity or GPS signal) results in a large number of small segments,

where many of these segments actually belong together.

This work proposes a novel segmentation approach that opportunistically fills gaps in a user's location trace by either borrowing location data from other co-located users utilizing the power of mobile crowd sensing and computing (MCSC) paradigm or by utilizing a user's personal data obtained from multiple sensor sources and devices such as the battery recharge behavior (measured on smartphones), step counts, and sleep patterns (measured by wearables).Through our analysis of four separate large-scale crowd sensing study datasets, we show that our approach able to generate fewer, but more complete segments than the state-of-the-art, where each segment accurately represents the presence of a user at a significant personal place.

Success!Xian Wu

Oral Candidacy Exam

April 22, 2019 9:00 am 384 Nieuwland

Adviser: Dr. Nitesh Chawla

Committee members:

Dr. Keith Feldman Dr. Meng Jiang

Dr. Christian Poellabauer

Title:

“Deep Learning for Time Series Analysis: from Methodology to Applications”

Abstract:

A time series is a sequential set of measurements collected at equally spaced intervals over a period of time. Analytical methods on time series have been widely applied in many areas, such as business market prediction, dynamic user activity understanding, and time-ordered wearable-sensory data modeling. With the advent of deep learning techniques, in this proposal, we strive to develop techniques based

on neural networks to tackle the key problem in modeling time series data. The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of complex temporal patterns from raw time series data. However, there exist two crucial challenges in time series analysis: i) the complex sequential transition regularities exhibited with time-dependent nature; ii) the multi-level periodicity of temporal patterns from time series data. To fully harness the power of time series data, this proposal aims to develop novel deep learning frameworks with the goal of discovering complex time-ordered sequential patterns and understanding multi-dimensional temporal dynamics. More specifically, work in this proposal investigates new methodologies for user activity prediction, time series forecasting and representation learning on wearable-sensory data. Accurate modeling of the underlying temporal dynamics from various time series data is the key to progress the areas of time series analysis.

Martin ImreOral Candidacy Exam

August 6, 2019 1:00 pm 315 StinsonAdviser: Dr. Chaoli Wang

Committee Members Dr. Hanqi Guo Dr. Ron Metoyer

Dr. Collin McMillan Title:

"GPU-ACCELERATED SUMMARIZATION AND RECONSTRUCTION FOR BIG

DATA ANALYSIS AND VISUALIZATION"

Abstract:

With the ever-growing amount of data we are capable of collecting nowadays, the need for methods to analyze and generate insight into the big data emerges .Data visualization offers a powerful tool to allow humans to better understand the data. However, the large-scale data generated through simulation or collected from real-world scenarios leads to an insurmountable amount to sift through for humans, even when the data are visualized in a concise format. To combat this, it is necessary to simplify the data using summarization techniques .While summarization allows an overview that is easier to digest for humans, it comes with the drawback of omitting parts of the data. To overcome this drawback, data reconstruction techniques allow for a level-of-detail analysis of the underlying data. They further make it possible to

synthesize missing data. For both data summarization and reconstruction, it is important to tackle different kinds of data in their respective ways. In this dissertation, I describe several ways to summarize and reconstruct time-varying multivariate, unsteady flow, and graph data.

Time-varying multivariate volumetric data typically stem from scientific simulations that describe physical or chemical processes, usually in either two or three spatial dimensions, a temporal dimension, and multiple variables. Volume visualization techniques are usually used visualize and analyze them. In this dissertation, I focus on data analysis using is surface rendering, a commonly used volume visualization technique. While is surface rendering allows detailed insight into time-varying multivariate data sets, the sheer complexity of them makes it often impossible to analyze and visualize all the data at once. A major challenge is the selection of interesting or important parts to bring to the attention of the analyst. Previous works have presented algorithms to select salient values, however, these algorithms do not scale well enough for big data analysis. In this dissertation, I present an acceleration and analysis framework to efficiently analyze and visualize complete large-scale time-varying multivariate data sets using is surfaces.

Success!Bingyu Shen

Oral Candidacy ExamAugust 5, 2019 11:00 am 315 Stinson

Adviser: Dr. Walter ScheirerCommittee Members:

Dr. David Chiang Dr. Adam Czajka Dr. Brian Price

Title:

“Advances in Style-Driven Machine Learning for Problems in Language and Vision”

Abstract:

Humans intuitively develop the ability to learn from multiple cognitive signals, such as vision and text. For instance, people naturally construct visual scenes in their minds throughout daily communication, and some people even memorize experiences and knowledge by visual sensations. Our brain associates visual content with text simultaneously, thus letting the modalities learn from each other, so should our deep learning-based artificial intelligence system.

In this work, I firstly explored traditional and state-of-art Natural Language Processing algorithms and applied them to real-world settings. Then I connected visual data with texts by integrating a language model into computer vision tasks. Furthermore, I would like to study the hidden relationships between visual and text in the remainder of my Ph.D. Specifically, the recent success of deep learning algorithms in 2D computer vision tasks inspired the application of deep neural networks into the 3D realms. As deep neural networks have shown the capability of representing images with features in latent space, it's made easier to generate 3D models directly from 2D images and further bridging the gap between synthetic 3D scenes and the real world. However, images are not the only guidance when creating 3D scenes. Human language descriptions can also convey rich information about scenes and resonated with audiences. Therefore, I propose to generate 3D scenes from natural language descriptions and refine the objects in the scene through further interactions. The goal is to automatically create a semantic 3D scene from textual description with fine details. The system should be able to infer the scene even without any specific commands or an explicit list of objects exist in the scene.

Success!Gonzalo Martinez

Oral Candidacy ExamAugust 12, 2019 12:00 pm 258 Fitzpatrick

Adviser: Dr. Aaron StriegelCommittee Members:

Dr. David Hachen    Dr. Christian Poellabauer Dr. Tim Weninger

Title:

"Increasing the Efficiency and Efficacy of Multi-Modal Longitudinal Sensing Studies"

Abstract:

Over the past few years, the quality and sophistication of wearable devices and smartphones have improved dramatically. The steady increase in the adoption of these devices has given researchers the opportunity to conduct research at a larger scale, outside of the laboratory and following less intrusive approaches to gathering real-time data from multiple sensors for a large number of participants. Simultaneously, the cloud has evolved to provide a significant data repository to support multi-modal sensing, gathering data from smartphones, social media, and wearables.

However, these multi-modal longitudinal studies are expensive because they usually require providing equipment, monetary incentives, and require substantive effort to recruit sizable and diverse populations. Because of the added cost and complexity of these studies, the focus of the dissertation will be to explore ways to maximize the data collected in the studies with minimum wasted effort and expense. To accomplish this goal, the dissertation will investigate how early can one anticipate whether a participant will be compliant and to what degree can participant compliance in studies can be improved. Furthermore, to increase the efficacy of the longitudinal study, the imputation of time-series data will be needed to deal with missing data due to unpredictable events such as breakages, bugs, and drops in compliance. In addition, there will be a need to carefully engineer features that fuse the data from the multiple sensors to provide an accurate characterization of the phenomenons that are being measured. To address these challenges, this proposal will introduce an example of this fusion of sensors for sleep detection as well as discuss an approach to impute time-series data based on collaborative filtering.

Success!Boyang Li

Oral Candidacy ExamAugust 20, 2019        10:00 am        315 Stinson

Adviser:  Dr. Yiyu ShiCommittee Members:

Dr. Taeho Jung      Dr. Walter Scheirer Dr. Jinjun XionG

Title:

"3D City Reconstruction with a Novel Consensus Managed Platform"

Abstract:

Bitcoin, as one of the most successful blockchain applications, attracts unprecedented attention and investment from both academia and industry. To maintain the consistency of transaction data, the proof-of-work consensus

mechanism utilizes the brute-force algorithm to host a competition of hardware and energy. To mine cryptocurrency more efficient, people developed GPU mining, FPGA mining, and ASIC machine mining. However, they still suffer from the issue of wasting energy. The proof-of-useful-work consensus has been proposed to execute relatively more useful tasks to maintain the consistency, therefore the ``wasted energy'' contribute to useful work.

On top of proof-of-useful-work consensus, I demonstrate the feasibility to exploit the computation power of blockchain for the deep learning algorithm. Because all miners are required to work on the same task in my previous work, it still wastes the majority of energy due to the computation redundancy.

In this proposal, I introduce three projects with the title of the Free market, Dynamic mining pool, and 3D city reconstruction with a novel consensus managed platform, respectively, for the rest of my Ph.D. research. The first two works aim to reduce the computation redundancy to calculate useful work more efficient. In the third work, I will introduce a novel consensus managed platform. This novel consensus will be more energy-efficient and more functional than the traditional proof-of-useful-work system.

Success!

Charles ZhengDissertation Defense

August 22, 2019   2:30 pm   258 FitzpatrickAdviser:  Dr. Douglas Thain

Committee Members:Dr. Lukas Rupprecht

Dr. Christian Poellabauer    Dr. Dong WangTitle:

“THE CHALLENGES OF SCALING UP HIGH-THROUGHPUT WORKFLOW WITH CONTAINER TECHNOLOGY”

High-throughput computing (HTC) is about using a large amount of computing resources over a long time to accomplish many independent and parallel computational tasks. HTC workloads are often described in the form of workflow and run on distributed systems through workflow systems. However, as most workflow systems are not liable for managing the task execution environment, HTC workflows are regularly limited in dedicated HTC facilities that have required settings.

Lately, container runtimes have been widely deployed across the public cloud because of its ability to deliver an execution environment with lower overheads than the virtual machine. This trend provides users of HTC workflows an opportunity to use unlimited computing power on the cloud. However, migrating complex workflow systems to a container environment is cumbersome.

To containerize HTC workflows and scale them up on the cloud, I synthesize my experiences on using container technologies and develop a methodology that contains seven design factors: i) Isolation Granularity – the granularity of isolation should be determined by characteristics for target workloads; ii) Container Management – container runtimes must be adapted to the distributed environment, and the under-layer distributed systems best does the management of containers; iii) Image Management – a cooperated mechanism can help to speed up and improve the efficiency of image distribution in a distributed environment; iv) Garbage Collection – timely garbage collection is necessary given the massive amount of intermediate data generated by the HTC workflow; v) Network Connection – excessive network connections should be avoided considering the plenty of small transmissions; vi) Resource Management – customized resource management mechanisms that fully consider the characteristics of the target workflow are required; vii) Cross-layer Cooperation – implementation of advanced features requires cooperation between the upper-layer workflow system and the under-layer cluster manager.

In addition to HTC workflows, I validate the above factors through my work of standardizing resource provisioning process for extreme-scale online workloads and observe that they are equally applicable to the HTC workflow as well as the extreme-scale online workload.

Gabriel WrightOral Candidacy Exam

September 20, 2019      1:30 pm      258 FitzpatrickAdvisers: 

Dr. Tijana Milenkovic and Dr. Scott EmrichCommittee Members:

Dr. Patricia Clark      Dr. Collin McMillan Dr. Tim Weninger

Title:"Computational Analyses of Codon Usage Bias and its Effects on Protein

Folding, Function, and Expression”

Synonymous codons (i.e., codons that code for the same amino acid) are not used uniformly within a species' genome, resulting in codon usage bias (CUB) for each species. Despite not affecting the resulting amino acid sequence, alterations to synonymous codon usage in a gene are known to affect the folding, function, and expression level of the resulting protein. One prominent theory on why this occurs is that a codon's translation rate is directly related to how "preferred" the codon is in that species, and that altered translation rates can potentially affect necessary co-translational protein folding. However, there is debate about how best to define CUB

so that it most accurately depicts codon translation rates. A better understanding of codon translation rates is imperative to understanding the processes of co-translational protein folding and heterologous gene expression. The debate about how best to define CUB has resulted in the development of numerous codon usage models, all of which define preferred codons in a distinct way. However, a rigorous comparison of these codon usage models has not yet been completed.

To this end, this proposal will complete a novel, rigorous comparison of these codon usage models relative to two different types of experimental data, as well as the development of two hybrid codon usage models that address a weakness of one of the most popular CUB models. We will also develop a novel 'codon harmonization' algorithm, whose aim is to replicate the codon usage bias of a native gene sequence in a synonymous mutant (for heterologous gene expression). This algorithm has the ability to incorporate various codon usage models, and therefore allows each model's biological efficacy to be tested in vivo. Finally, we propose a novel framework for evaluating codon usage relative to 3D protein structures. This framework aims to improve analysis of the effects of hypothesized co-translational folding on resulting protein structures

Success!Shenglong Zhu

Dissertation DefenseOctober 7, 2019    1:30 pm     258 Fitzpatrick

Adivsers:  Dr. Danny Chen and Dr. Scott Emrich Committee Members:

Dr. Gregory Madey      Dr. Meng Jiang     

Dr. Taeho Jung

Title:

"Algorithms for Assembly Consolidation and Prediction of Large-Scale Genome Structure"Abstract:

Genome structure is the order and orientation of pieces of DNA comprising a genome, which contains the information of life. With advances in DNA sequencing technology and now massive availability of sequence data, the study of genome structure cannot be easily carried out without efficient and expressly designed algorithms. In this dissertation, we study three genome structure-related problems: structural error correction of draft genome assemblies, inversion prediction, and predicting operons. Our work with draft genome assemblies explores a novel Maximum Alternating Path Cover (MAPC) model to improve genome correctness and downstream analysis. Our work on inversion prediction aims to predict and catalog inversions by exploring the well-known Range Maximum Query model and Max-Cut model for what we call "global" inversions, and the novel Rectangle Clustering model and Representative Rectangle Prediction model for more localized inversions. For operon prediction, we again apply the MAPC model (with improved algorithms and theoretical analysis), coupled with a novel Intro-Column Exclusive Clustering model, to predict and catalog operons in closely related species. Evaluated using both simulated and real genome data, our algorithms and implementations have shown substantial promise for accurate computational analysis of genome structure in significantly shortened time.

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