Software platform engages communities in school rezoning decisions

Left to right: Colin Flynn, Vicki Keegan, and Susan Hembach from Loudoun County Public Schools meet at the Discovery Analytics Center with Ph.D. students Andreea Sistrunk, Subhodip Biswas, and Fanglan Chen to discuss how Redistrict is helping to establish school attendance zones.

School rezoning decisions often cause emotional stress for families and communities for a variety of reasons.

Parents worry about continuity of programs and activities at a new school, the toll it might take on their children’s friendships, and modes of transportation. School officials, administrators, and staff want to ensure that all students have equitable access to educational programs and facilities. Almost everyone is concerned about the impact a particular school attendance zone will have on traffic patterns, especially at opening and closing times.

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DAC Student Spotlight: Payel Bandyopadhyay

Payel Bandyopadhyay, DAC Ph.D. student computer science

Payel Bandyopadhyay is trying to understand the role of 3D immersive environment (an artificial, interactive, computer-created scene or “world” within which a user can immerse themselves) for sensemaking in textual data.

According to Bandyopadhyay, prior work at Virginia Tech by her advisor Chris North and others has shown the part that 2D space plays in sensemaking. Her current research investigates 3D immersive environments to determine if they provide any additional benefit or not.

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DAC Student Spotlight: Lulwah AlKulaib

Lulwah AlKulaib, DAC Ph.D. student in computer science

While a master’s degree student in computer science at George Washington University, Lulwah AlKulaib would look for published papers in high impact factor journals and highly respected, top rated conferences matching her field of interest, machine learning.

“This is where I first learned about the Discovery Analytics Center, the research being done there, and that it was located in northern Virginia as well as in Blacksburg,” she said.

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Discovery Analytics Center study sheds light on what turns a peaceful protest into a violent one

Protest in Brazil

Protests are an increasingly common occurrence, but only a small percentage of them turn violent. In a collaborative study led by the Discovery Analytics Center with the University of California, San Diego, and George Mason University, a team of researchers set out to uncover triggers that foretell violence by crowds.

Gathering data from thousands of online news sources in five Latin American countries — Argentina, Brazil, Colombia, Paraguay, and Venezuela — the researchers used the characteristics of past events to develop new methods that forecast the occurrence of violent crowd behavior in advance.

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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.

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Winning Blockchain Challenge team includes DAC student Arjun Choudhry

From left to right: Ikechukwu Dimobi, Arjun Choudhry, and Zachary Gould

A three-member student-driven team that includes Arjun Choudhry, a student at the Discovery Analytics Center, has won first place in the design phase of the Virginia Tech Blockchain Challenge led by the Department of Computer Science and made possible in part through a generous gift from, a leader in providing high-performance blockchain solutions. The award carries a $1,000 prize.

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DAC Student Spotlight: Shuo Lei

Shuo Lei, DAC Ph.D. student in computer science

A Ph.D. student in computer science, Shuo Lei is focusing her research on few-shot learning and robust model learning. She is advised by Chang-Tien Lu.

“The aim of AI is to train machines to do some of the work that people were needed to do previously,” said Lei. “The training process requires a large amount of labeled data. It is time intensive and there are significant labor costs in collecting and labeling all that data. Few-shot learning can be valuable in forwarding research because it reduces the training cost by using less labeled data to get the same – and sometimes even greater – accuracy in training results.

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DAC Student Spotlight: Moeti Masiane

Moeti Masiane, DAC Ph.D. student in computer science

Moeti Masiane’s initial interest in analyzing data grew even stronger when earning a bachelor’s degree in computer science from the University of the District of Columbia and then a master’s degree from Norfolk State University.

As he began to consider going on to a Ph.D. program in the same field, he was drawn to Virginia Tech and the Discovery Analytics Center. “The expert DAC faculty really made me want to be part of the team,” said Masiane, who is advised by Chris North.

He has been at DAC since 2016, where, he said, “I  am surrounded by talented faculty and students who are always willing to suggest new ways of solving data analysis-related challenges.”

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DAC Student Spotlight: Ming Zhu

Ming Zhu, DAC Ph.D. student in computer science

Ming Zhu learned about machine reading comprehension — making computers understand sophisticated natural language and be able to answer questions about what was read — while taking a graduate course at Carnegie Mellon University.

“After building a state-of-the-art Neural Question Answering (QA) model from scratch based on a research paper, my confidence grew in believing I could be a part of this future technology and pushed me further to focus my Ph.D. research in this area,” said Zhu.

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DAC Student Spotlight: John Wenskovitch

John Wenskovitch, DAC Ph.D. student in computer science

John Wenskovitch’s research interest is centered around the idea of creating interactive visualization systems that learn from user interactions. This often takes the form of conducting exploratory data analysis on high-dimensional, numerical datasets and using a common visualization technique, 2D scatterplot, to project the data.

When asked if he could explain his work to someone not in the computer science field, Wenskovitch, a Ph.D. student at the Discovery Analytics Center, turned to the stars.

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