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.
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.
Shuangfei Fan, DAC Ph.D. student in the Department of Computer Science
Graphic is from Fan’s research on “Deep Generative Models for Generating Labeled Graphs”
In disease control and prevention, understanding how an emerging infectious disease can spread beyond the visible network is important.
Marketers posting ads on an online social network can benefit from knowing how their information will spread beyond ego networks.
These two scenarios provide good examples for practical application of Shuangfei Fan’s research using deep representation learning algorithms on labeled graphs to model graph generation and graph evolution.
Fanglan Chen, UrbComp and DAC Ph.D. student in the Department of Computer Science
Graphic is from Chen’s research on “Mitigating Uncertainty in Document Classification”
Motivated to improve the health and quality of urban environments through new data sources and methods, Fanglan Chen is simultaneously pursuing a Ph.D. in computer science and a master’s degree in urban planning.
Alexander Rodriguez, DAC Ph.D. student in the Department of Computer Science
Alexander Rodriguez was a master’s degree student in data science at the University of Oklahoma when he met B. Aditya Prakash at the 2017 ACM SIGKDD Conference on Knowledge Discovery and Data Mining. That meeting sealed his decision to apply to the Ph.D. computer science program at Virginia Tech.
“I was excited about the problems he was working on and I felt he was a knowledgeable person from whom I could learn how to become a researcher,” said Rodriguez of his current advisor.
Alyssa Herbst, DAC M.S. student in the Department of Computer Science
Graphic is from Herbst’s research on “Active Learning by Greedy Split and Label Exploration”
After receiving a master’s degree in computer science at Fall Commencement, Alyssa Herbst will head to New York City. She has already accepted a position as software engineer at Instagram, where she interned on the Shopping Machine Learning team this past summer.
Herbst’s interest in machine learning sparked when, as an undergrad in the Department of Computer Science, she took a class taught by Bert Huang. She wound up working in Huang’s Machine Learning Laboratory on a twitter scraping project to assist with cyberbullying research.
“As part of this research, we had a corpus of tweets that we wanted to label as either ‘bullying’ or ‘not bullying,’ but a limited crowdsourcing budget. So we started to think about what it would look like to ‘guess’ the labels of tweets with some degree of certainty if crowdsource workers labeled some of the tweets,” said Herbst.
Mahmud Shahriar Hossain was recently promoted to associate professor of computer science, with tenure, at the University of Texas at El Paso (UTEP). He leads the university’s Discovery Analytics Lab. Hossain earned his Ph.D. in computer science from Virginia Tech in 2012 and joined UTEP as an assistant professor in 2013.
While at the Discovery Analytics Center at Virginia Tech, his work with advisor, Naren Ramakrishnan focused on event analysis, “storytelling,” and data abstraction techniques like alternative clustering and scatter/gather clustering. He applied his methods to solve a broad spectrum of problems in multiple disciplines, including national security, biomedical science, and mechanical engineering.
Shane Bookhultz, DAC and UrbComp Ph.D. student in the Department of Statistics
Graphic is from Bookhultz’s poster presentation “Measuring Polarity from News Sources: a Topic Modeling Approach”
“Working with text data is challenging but that is what I like about it,” said Shane Bookhultz, a Ph.D. student in the Department of Statistics. “It is inherently noisy because of different mannerisms, choices of words, and tones. Unlike numerical data, which is pretty concrete, text data can have various interpretations.”
Jianfeng He, DAC Ph.D. student in computer science
Along his educational path, Jianfeng He learned an important lesson: Having a good advisor should be the number one priority in choosing a Ph.D. program. The opportunity to work with Chang-Tien Lu drew him to Virginia Tech and the Discovery Analytics Center after spending a short period of time at another university.
He’s focus is on data analysis of social media and he is currently working on image editing based on user requests and text classification based on machine learning.
This research builds upon an interest that began while He was an undergraduate majoring in digital media technology at the Central China Normal University and required to learn media design software, including Adobe Photoshop, Adobe Premiere, and MAYA.