DAC Student Spotlight: Nikhil Muralidhar

Nikhil Muralidhar, DAC and UrbComp Ph.D. student in the Department of Computer Science

Graphic is from Muralidhar’s paper on “PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly”

Choosing to pursue a Ph.D. in computer science at Virginia Tech was easy for Nikhil Muralidhar.

“Virginia Tech was my top choice for good reason,” Muralidhar said. “It is known for its quality research and interdepartmental collaborations, for encouraging students to work on real world interdisciplinary applications, and for pioneering programs like UrbComp.”

Also factoring in his decision was the opportunity to join the Discovery Analytics Center.

“I had been following DAC’s track record of high quality, practical research since I was a Virginia Tech undergraduate. I am happy to be part of a rare breed of research labs with both extensive industrial and academic collaborations. The facilities are state-of-the-art and the faculty are approachable, helpful, and use their experience to guide their students to become successful researchers,” said Muralidhar, who is advised by Naren Ramakrishnan.

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DAC Student Spotlight: Lei Zhang

Lei Zhang, DAC Ph.D. student in the Department of Computer Science

Graphic is from Zhang’s research on “Situation-Based Interpretable Learning for Personality Prediction in Social Media”

Lei Zhang was a master’s degree student in software engineering at Jinan University in China when his advisor told him about meeting Chang-Tien Lu from Virginia Tech and how he was doing research with algorithms on Twitter. While they were using different platforms — Zhang’s own work was on Weibo, the largest Chinese microblogging website — he was interested to hear  about Lu’s research.

When he decided to pursue a Ph.D., Zhang decided to apply to Virginia Tech’s Department of  Computer Science. As it turned out, Lu is now his advisor.

Zhang’s current research at the Discovery Analytics Center includes graph structure learning.

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DAC Student Spotlight: Mohammad Raihanul Islam

Mohammad Raihanul Islam, DAC Ph.D. student in the Department of Computer Science

Graphic is from Islam’s paper on “RumorSleuth: joint detection of rumor veracity and user stance”

Classifying rumors and fake news in social media is the focus of Mohammad Raihanul Islam’s work at the Discovery Analytics Center.

“A rumor generally refers to an interesting piece of information — widely disseminated through a social network — that is not easy to substantiate,” said Islam, a Ph.D. student in computer science.

Later, it can turn out to be true, false, or remain unverified.

“The threat of rumors and fake news is very real and identification is crucial because rumors and fake news can lead to deleterious effects on users and society,” he said. “For example, spreading unverified malicious content could cause severe economic downfalls within a short period of time.”

The objective of his research, he said, is to develop a range of machine learning methods to effectively detect and characterize rumor veracity in social media.

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DAC Student Spotlight: Debanjan Datta

Debanjan Datta, DAC Ph.D. student in the Department of Computer Science

Graphic is from Datta’s paper on ”Detecting Suspicious Timber Trades”

Debanjan Datta’s interest in data mining focuses on systems that perform anomaly detection with both interpretability and the ability to incorporate domain knowledge and human input.

In a recent Discovery Analytics Center study with the World Wildlife Fund, Datta developed a framework that can apply machine learning on massive trade datasets to detect patterns of suspicious timber records that relate to possible illegal trade. He shared results of the study, “Detecting Suspicious Timber Trades,” at the Conference on Innovative Applications of Artificial Intelligence (IAAI) earlier this month.

The research involves analyzing, record-by-record, thousands of lines of export and import data.

“By analyzing available timber data, along with open source domain knowledge, we are trying to develop software and algorithms that will help flag suspicious timber at the border in real time. Such a human-machine approach can improve both efficiency and effectiveness,” Datta said.

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DAC Student Spotlight: Sneha Mehta

Sneha Mehta, DAC Ph.D. student in the Department of Computer Science

Graphic is from Mehta’s paper on “Event Detection using Hierarchical Multi-Aspect Attention”

Sneha Mehta, a Ph.D. student in computer science at the Discovery Analytics Center, was in New York City this week to present “Simplify-then-Translate: Automatic Preprocessing for Black-Box Translation” in a talk and poster presentation at the main AAAI Conference on Artificial Intelligence.

The paper represents her work on novel methods to improve machine translation for subtitles while an intern at Netflix for two consecutive summers.

In fact, last summer was a busy one for Mehta, who is advised by Naren Ramakrishnan. In addition to an internship at Neflix headquarters in Los Gatos, California, she was also selected to attend the Deep Learning and Reinforcement Learning Summer School (DLRLSS), in Edmonton, Alberta, Canada.

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DAC Student Spotlight: Abdulaziz Alhamadani

Abdulaziz Alhamadani, DAC Ph.D. student in the Department of Computer Science

Graphic is from Alhamadani’s paper “Batman or the Joker? The Powerful Urban Computing and its Ethics Issues”

Abdulaziz Alhamadani’s path to computer science is somewhat atypical.

Having already earned a bachelor of arts degree in English language from Umm Al-Qura University and a master of arts in English literature from King AbdulAziz University, Alhamadani made a decision to combine his knowledge of linguistics with computer science. That resolve led him to the University of New Hampshire, where he earned a master of science degree in computer science.

Now, as a Ph.D. student in computer science at the Discovery Analytics Center,  Alhamadani is focusing on Arabic natural language processing, especially text summarization and text classification. Advised by Chang-Tien Lu, his work involves automatic archiving of news without human annotation and summarizing daily news articles to headlines.

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DAC Student Spotlight: Jinwoo Choi

Graphic is from Choi’s paper on “Why Can’t I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition”

Jinwoo Choi, DAC Ph.D. student in the department of Electrical and Computer Engineering

 

 

 

 

 

 

 

 

 

 

Jinwoo Choi will be heading to Snowmass Village, Colorado, in March to present “Unsupervised and Semi-Supervised Domain Adaptation for Action Recognition from Drones” during the 2020 Winter Conference on Applications of Computer Vision. WACV is a premier meeting of the IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence.

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Congratulations to DAC summer and fall 2019 graduates!

Chris North (left), associate director of DAC and professor of computer science, with John Wenskovitch (right), DAC Ph.D. graduate at the Fall 2019 commencement ceremony

Virginia Tech’s Fall Commencement ceremony was held on Friday, Dec. 20.

New summer/fall alumni include four Ph.D. students and one master’s student at the Discovery Analytics Center.

“We are very proud of our graduates and the impactful research they have undertaken at DAC while pursuing their graduate degrees,” said Naren Ramakrishnan, the Thomas L. Phillips Professor of Engineering and director of the center. “We wish them continued success as they embark on their academic and industry careers.”

Following are the DAC graduates: 

Shuangfei Fan, advised by Bert Huang, received a Ph.D. in computer science. Her research interests are machine learning, graph analysis and deep learning, and her dissertation title is “Deep Representation Learning on Labeled Graphs.” Fan joins Facebook as a research scientist. In that position she will work to apply machine learning techniques to help people build community and bring the world closer together.

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DAC Student Spotlight: Anika Tabassum

Anika Tabassum, DAC and UrbComp student in the Department of Computer Science

Graphic is from Tabassum’s paper on “Urban-Net: A System to Understand and Analyze Critical Emergency Management”

 

 

 

 

 

 

 

Urban computing plays a large part in Anika Tabassum’s research at the Discovery Analytics Center as she attempts to answer questions related to critical infrastructure systems: Which power grids/substations are most vulnerable and need immediate action to recover during a hurricane? Which regions are highly affected during a power outage? Are there patterns or similarities in power outages among the connected components?

Tabassum uses optimization and learning-based algorithms when trying to solve energy challenges like these. A Ph.D. student in computer science, she is also a research trainee in the National Science Foundation-sponsored UrbComp graduate certificate program, which is administered through DAC.

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Focus on Wei Wang…..a DAC alumnus interview

Wei Wang, DAC alumnus

Wei Wang graduated with a Ph.D. in computer science in 2017 and joined the Language and Information Technology (LIT) group at Microsoft Research, Redmond, Washington, as an applied scientist. Recently, he was promoted to senior applied scientist.

Did transitioning from academia to industry hold any real surprises for you?

For the most part, problems that we try to solve as Ph.D. students are well-defined and have benchmarks. We just need to propose novel approaches to push the-state-of-the-art. The problems I face now often require much more effort to build an end-to-end solution.

What are your responsibilities at Microsoft Research?

I mainly work in the area of natural language understanding and user behavior modeling. I also collaborate with the product team to transfer state-of-the-art technique to the product.

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