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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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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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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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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 Block.one, a leader in providing high-performance blockchain solutions. The award carries a $1,000 prize.

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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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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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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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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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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.”

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How do we form our opinions? How do we develop the mental models that make us different and unique?

Finding answers to these questions is what drives Tianyi Li’s research at the Discovery Analytics Center. As a Ph.D. student in computer science, her research interests include human-computer interaction (HCI), collective (crowdsourced) intelligence, visual analytics, and explainable artificial intelligence (AI).

“I have always been interested in human cognition and intelligence, especially the sensemaking process,” said Li, who is advised by Chris North at DAC and co-advised by Kurt Luther. “Studying computer science during my undergrad years at Hong Kong University made me think deeper about the relationship between human and computing intelligence. I am excited by how much computer science has been advancing our understanding of the black box of human intelligence by developing smarter and human-friendly technologies.”

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