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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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Anuj Karpatne, an assistant professor of computer science and a Discovery Analytics Center faculty member, has been named the 2019 Intelligent Systems and Geoscience (IS-GEO) inaugural Fellow.

The announcement was made by Suzanne Pierce, a research scientist at the Texas Advanced Computing Center (TACC) and principal investigator for the IS-GEO Research Coordination Network, during the American Association for the Advancement of Science (AAAS) conference in Washington, D.C., today.

“I am pleased to announce the IS-GEO Fellows Program,” Pierce said. “The program is designed to support researchers as they commit to in-depth projects to accelerate discoveries. Dr. Karpatne was selected because of his expertise in scientific theory{or physics}-guided machine learning. Throughout his fellowship year, he will evaluate applied machine learning approaches to Earth datasets for the energy industry.”

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Bijaya Adhikari, a Ph.D. student in computer science, was attracted to the Discovery Analytics Center by the opportunity to solve data mining problems that are not only theoretically interesting, but have real-world applications, as well.

Adhikari’s core research focuses on graph mining and topics relating to social network analysis, such as community detection, immunization, influence maximization, and information. His interests also lie in machine learning, theoretical computer science, and algorithms.

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Current Virginia Tech graduate students Mohammed Almannaa, Fanglan Chen, and Swapna Thorve have earned the Urban Computing (UrbComp) certificate, a cross disciplinary program sponsored by the National Science Foundation and led by the Discovery Analytics Center.

The program is a collaboration between eight departments and five colleges and trains students to use both foundational and applied aspects of data science to help solve problems related to urban issues like transportation, affordable housing, and policing.

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Discovery Analytics Center faculty Bert Huang and Chandan Reddy and two Ph.D. students were in Honolulu, Hawaii, last week, sharing their research with attendees at the Thirty-Third AAAI Conference on Artificial Intelligence.

Chidubem Arachie and You Lu, both in the Department of Computer Science, presented spotlight talks on studies they collaborated on with Huang, who is their advisor. The studies were also included in a poster session.

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