News featuring Layne T. Watson

DAC Student Spotlight: Joshua Detwiler

Joshua Detwiler, DAC Ph.D. student in the Department of Computer Science

Joshua Detwiler, a Ph.D. student in computer science, believes that his research interest in solving automated redistricting/gerrymandering as an optimization problem is well aligned with both the applied and interdisciplinary focus at the Discovery Analytics Center and with what he is learning as a research trainee in the National Science Foundation-sponsored urban computing graduate certificate program, administered through DAC.

“I have always wanted to work on applied research that would help solve a large real-world problem,” said Detwiler, who is advised by Layne Watson, a professor in the departments of computer science, mathematics, and aerospace and ocean engineering. “Gerrymandering is both a solution and problem to real world-political redistricting, school redistricting, and other similar applications.”


In political redistricting, he said, it answers questions such as how to represent a population in the U.S. House of Representatives, where multiple representatives are elected for each state. In school redistricting, neighborhood-like areas are assigned to city or county schools in order
to utilize school capacities and address other concerns like having diversity in schools.

“However,” said Detwiler, “gerrymandering is often under scrutiny as a historically manual process where individuals might draw boundaries to serve a different agenda,  My research aims to bridge some of the current gaps in numerical optimization software that will also better solve for an optimal redistricting plan, namely by tackling computationally difficult constraints like the connectivity of district constituents.”

Detwiler earned bachelor degrees in both computer science and mathematics from Virginia Tech in December of 2019. During his last two summers as an undergraduate, he interned at the U.S. Department of the Navy where he worked on automating a cybersecurity benchmark for the Red Hat Linux Entry-level Server 4 (RHEL 4) operating system. He also wrote unit tests and user interface (UI) tests for Java components in legacy software and successfully presented automated testing principles to the software team to change our organization culture that had absence of testing.

This past summer, Detwiler returned to the Navy as an intern, working remotely due to COVID-19. He worked on a distributed application that could provide application layer network performance metrics.

He is projected to graduate in 2024. His career goal lies in academia, where he can combine teaching and research as a professor.


Congratulations to our Ph.D. and master’s degree Spring graduates at the Discovery Analytics Center!

DAC graduates include (left to right): Xuchao Zhang with advisor Chang-Tien Lu in the National Capital Region; and Elaheh Raisi with Bert Huang and Yufeng Ma hooded by Ed Fox, both in Blacksburg.

The Discovery Analytics Center is pleased to announce that five of their Ph.D. and four of their master’s degree students celebrated graduation from Virginia Tech last weekend at Commencement ceremonies in Blacksburg and in the National Capital Region.

“It is always bittersweet to bid our students farewell, but we wish them all the best. We know and appreciate how hard they have worked to achieve the high goals they set for themselves and look forward to following their successful careers in academia and industry,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center.

 

Ph.D. graduates

Sorour E. Amiri, advised by B. Aditya Prakash, received a Ph.D. in computer science.
Her research interests are large-scale graph mining, data mining, and applied machine learning and the title of her dissertation is “Task-specific Summarization of Networks: Optimization and Learning.” She is joining the Google search ad team.

Minghan Chen, co-advised by Layne Watson, received a Ph.D. in computer science. Her research interest is computational cell biology and her dissertation title is “Stochastic Modeling and Simulation of Multiscale Biochemical Networks.” She joins the Computer Science Department at Wake Forest University as assistant professor.

Yufeng Ma, co-advised by Weiguo (Patrick) Fan and Edward Fox, received a Ph.D. in computer science. Ma’s research interests are computer vision, Natural Language Processing (NLP), and deep learning and his dissertation title is “Going Deeper with Images and Natural Language.” Ma is joining Verizon Media (Yahoo! Research) as a research scientist focusing on personalized recommendations.

Elaheh Raisi, advised by Bert Huang, received a Ph.D. in computer science. Her research interests are machine learning, weakly supervised learning, and computational social science and her dissertation title is “Weakly Supervised Machine Learning for Cyberbullying Detection.”

Xuchao Zhang, advised by Chang-Tien Lu, received a Ph.D. in computer science. His research interests are data mining, machine learning, and Natural Language Processing (NLP) and his dissertation title is “Scalable Robust Models Under Adversarial Data Corruption.”  Zhang joins NEC Labs America as a researcher. In that position he will work to fully understand the dynamics of big data from complex systems; retrieve patterns to profile them; and build innovative solutions to help end user managing those systems.

Master’s graduates

Raja Venkata Satya Phanindra Chava, advised by Edward Fox, received a master of engineering degree. His research interests are text summarization using deep learning and Natural Language Processing (NLP) and his project title is “Natural Language Processing Techniques for Comprehending Legal Depositions.” Chava is joining Walmart in Reston, Virginia, as a software engineer and will work on big data analysis to manage the supply chain and personalize the customer’s shopping experience.

Supritha B. Patil, advised by Edward Fox, received a master of science degree in computer science. Patil’s research interest is Natural Language Processing (NPL) and her thesis title is “Analysis of Moving Events Using Tweets.” She will be working as a software developer.

Adithya Upadhya, advised by Edward Fox, received a master’s in computer science. His research interests are machine learning and high performance computing and his project title is “A General Web Platform Summarizing Text and Documents.”

Xinfeng Xu, advised by B. Aditya Prakash, received a master’s degree in computer science. His research focused on modeling and predicting incidence and the title of his thesis is “Modeling and Predicting Incidence: Critical Systems Failures and Flu Infection Cases.” He also received the 2019 MS Research Award from the Department of Science. Xu is also a Ph.D. student in physics in the College of Science and will continue his research in that field.

 

 

 

 


DAC Student Spotlight: Tyler Chang

Tyler Chang, DAC Ph.D. student in computer science

The spring semester has brought some good news for Tyler Chang, a Ph.D. student at the Discovery Analytics Center.  In June, he will begin a six-month appointment at Argonne National Lab in Washington, D.C., where he will continue to work on his dissertation while applying his work to a new set of problems relevant to the U.S. Department of Energy.

Chang, a computer science major specializing in numerical analysis, is focusing his research on interpolation and nonconvex optimization.  His advisor is Layne Watson.

The interpolation problem is to predict values between data points. “Given the total revenue earned by some small businesses and numerical descriptions of their marketing strategies, one might interpolate to predict revenue that will be earned by a new business with its own marketing strategy,” said Chang.

“The optimization problem is to find a best configuration by choosing where to sample new data points. For example,” he said, “when designing an aircraft, each design produces some amount of lift. So, an aircraft engineer might use optimization to search for the particular design that produces the maximum lift.”

His research is partially funded by the VarSys project, an interdisciplinary effort to understand and model performance variance in computer systems. The motivation for this project is that small fluctuations in the throughput, energy consumption, etc., of large machines can have significant consequences for computer system performance, behavior, and even security.

Chang said that while this may seem far removed from his research, the VarSys project can boil down to gathering performance data and then predicting performance statistics for new system configurations (the interpolation problem) and even searching for system configurations that minimize or maximize performance statistics (the optimization problem).

His bachelor’s degree from Virginia Wesleyan College is in mathematics and computer science. While an undergraduate, Chang  held a number of research internships spanning computer vision, circuit/hardware design, information visualization, autonomous driving, and parallel computing.

“As a double major, I was always looking for opportunities to apply both of my skills,” said Chang. “I discovered numerical analysis while working at Old Dominion University on a NASA grant involving computational fluid dynamics, work I initially found to be extremely challenging. But it offered the perfect marriage of passion for mathematics and computer science. My current research in interpolation and optimization allows me to channel my interest in those two fields into helping to solve a wide variety of engineering design and data science problems.”

Chang is first author on three conference papers: “Computing the Umbrella Neighbourhood of a Vertex in the Delaunay Triangulation and a Single Voronoi Cell in Arbitrary Dimension,” IEEE Southeast Con 2018; “A Polynomial Time Algorithm for Multivariate Interpolation in Arbitrary Dimension via the Delaunay Triangulation,” in Proceedings of the ACMSE 2018 Conference; and “Predicting System Performance by Interpolation Using a High-dimensional Delaunay Triangulation,” in Proceedings of the High Performance Computing Symposium (HPC ’18), Society for Computer Simulation International. Chang has also co-authored four additional conference papers.

“I love the interdisciplinary aspects of my research at DAC,” said Chang. “Through collaboration, I learn about, and even contribute to, cutting-edge research in statistics, computer systems, computer security, engineering, and other fascinating fields, all while continuing to hone my skills in numerical analysis.”

When Chang has free time he enjoys weight lifting and playing 80s and 90s rock tunes on his keyboard. While he played varsity tennis as an undergraduate, his interest in tennis is now as a fan of his sister Sophie, who is currently a top 500 professional tennis player.

Projected to graduate in 2020, Chang said he would be happy with a career in either industry or academia. “But,” he said, “having great experiences working for government labs, I would consider a position with a national lab to be my top career goal.”


DAC Student Spotlight: Thomas Lux

Thomas Lux, DAC Ph.D. student in computer science

Thomas Lux does not hesitate when it comes to setting long-term goals.

“After graduation I would like to work somewhere that allows me to devote my time to pursuing research in artificial general intelligence,” he said. “I can easily see myself at an industry/government lab, in academia, or in a small startup. I will be happy as long as I get to contribute to the creation of super-human intelligent algorithms that can benefit people in society.”

Lux, a computer science student in the Discovery Analytics Center and a research trainee in the National Science Foundation-sponsored Urban Computing certificate program, an interdisciplinary program administered through DAC, would also like to use his data analytics skills to collaborate and contribute to his fiancé’s work in neuropsychological assessment. (She is pursuing a Ph.D. in clinical neuropsychology at Saint Louis University.)

In choosing a Ph.D. program, he was particularly interested in working with faculty like his advisor, Layne Watson, who have strong backgrounds in mathematics and optimization, and whose research is grounded in practical applied problems.

“I think the best way to solve problems is by learning to combine existing theory with real-world constraints in order to develop new theory tailored for specific applications,” Lux said.

Lux’s research focuses on applied approximation, numerical analysis, and nonparametric statistics. He is part of the VarSys team that creates models to help understand and manage computational performance variability across the computer system stack.

Petascale — and inevitably exascale — computing comes with many hurdles, he said, including immense losses in performance that result from interactions between parts of computers. The team collects data and builds models that address questions like: How do you configure an operating system to minimize energy consumption? What file and record sizes should be used to minimize read throughput variance? What CPU cache hierarchy is least vulnerable to side channel attacks?

Lux is committed to contributing to this field of study and this summer will be his third consecutive doing research at VarSys.

“Although the systems-oriented application that I work on may sound far off, the underlying mathematical concepts are surprisingly similar. In order to invent intelligent learning algorithms, we must understand the limits and maximize the performance of models we build over data. My theoretical work for systems is laying a foundation of robust, mathematically justified, and explainable algorithms for creating learning machines,” Lux said.

Lux’s research as first author has been included in a number of conference proceedings. Among them are: Nonparametric Distribution Models for Predicting and Managing Computational Performance Variability, IEEE SoutheastCon 2018; Predictive Modeling of I/O Characteristics in High Performance Computing Systems, Association of Computing Machinery High Performance Computing Symposium, April 2018; and Novel Meshes for Multivariate Interpolation and Approximation, 2018 Association of Computing Machinery Southeast Conference.

He has also coauthored a number of papers.

When not busy working on his research, Lux enjoys active sports — soccer, frisbee golf, and racquetball — and hiking the mountains of southwest Virginia. Formally trained in jazz percussion and drums, he said he loves music but now mostly plays piano and guitar.

Lux’s projected graduation is May 2020.

 

 

 


NSF funds UrbComp, program focused on big data and urbanization

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DAC will create and administer a new interdisciplinary Ph.D. certificate program called UrbComp, which is set to launch in spring 2016.  The UrbComp Ph.D. certificate is focused on big data and urbanization through a $3 grant over five years from the National Science Foundation Research Traineeship Program. UrbComp will be open to students from both the Blackburg and National Capital Region campuses who are pursuing a Ph.D. in one of eight departments: computer science, mathematics, statistics, electrical and computer engineering, population health sciences, urban affairs and planning, civil and environmental engineering, or sociology. To read more about the program click here.