The Sanghani Center is home to high-profile research, garnering recognition within and beyond the data analytics community.
Our talented team has been recognized with many competitive research awards and featured in major news and media outlets such as the Wall Street Journal, Newsweek, the Boston Globe and the Chronicle of Higher Education.
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.
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.
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.
“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.
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.
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.
Sirui Yao, DAC Ph.D. student in the Department of Computer Science
Graphic is from Yao’s NeurIPS 2017 paper “Beyond Parity: Fairness Objectives for Collaborative Filtering”
Sirui Yao studies the biases of recommender systems.
“A recommender will often suggest different courses to male and female college students because based on historical data, there are differences in course preference between these two groups,” said Yao, a Ph.D. student in computer science at the Discovery Analytics Center.
Taoran Ji, DAC Ph.D. student in the Department of Computer Science
Graphic is from Ji’s paper on “Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks”
Interested in data mining and machine learning, Taoran Ji, a Ph.D. student in computer science, said he was drawn to the Discovery Analytics Center because it plays an active role in these fields.
“There are so many projects at the center that provide great opportunities to practice these techniques in real world applications,” Ji said.
Ji, advised by Chang-Tien Lu, has focused his research on a range of topics, all of which he has been able to explore by collaborating with Lu and other faculty and students at DAC. These include event detection/prediction and associated applications such as civil unrest detection, airport threat detection, transit disruption detection, and emerging science and technology prediction.
Among his published papers, two were included in proceedings at conferences held this year.
Srijan Sengupta, DAC faculty member in the department of statistics
Reports that medical errors are the third leading cause of death in the United States have led the Institute of Medicine and several state legislatures to suggest that data from patient safety event reporting systems could help health care providers better understand safety hazards and, ultimately, improve patient care.
Sengupta has received an $815,218 Research Project Grant (R01) from the National Institutes of Health to develop novel statistical methods to analyze such unstructured data in safety reports. Click here to read more about Senputa’s grant.