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 inaugural academy inductees, drawn from academia, industry, and beyond, are principal leaders in information retrieval whose significant contributions have shaped the discipline or industry. Click here to read more about Edward’s inauguration.
Ph.D. student Jesse Harden’s current
research is focused on large,
high-resolution displays and their use in and benefits for data science.
“I am particularly interested in how we can better design software for large displays for data science. And in the future, I hope to look into how machine learning can be used to improve interactions with large screen UIs for both individual and collaborative use scenario,” said Harden, whose concentration in this area was influenced by reading the past works of his advisor, Chris North, and through their subsequent discussions.
Having earned two bachelor of science degrees, one in mathematics and one in engineering, and a master’s degree in informatics, all from UniversidadCatólica del Norte, Chile, Brian Keith was looking for a flexible Ph.D. program. The Virginia Tech Department of Computer Science provided that flexibility and led him to the Sanghani Center where interdisciplinary data science is a key focus.
In his Ph.D. research, Keith, co-advised by Chris North and Tanushree Mitra — is exploring online information narratives, in particular, how to represent, extract, and visualize them. He is also analyzing the issue of how misinformation spreads in these narratives.
According to Yali Bian,
Center’s proclivity for
encouraging interdisciplinary research
is an added benefit while working on his dissertation topic, “Interactive Deep Learning for Semantic
Interaction.” It encompasses several different research areas like human
computer interaction, deep learning, visual analytics, and explainable AI.
Bian is exploring ways to provide user-friendly interactive visualization systems to users unfamiliar with deep learning so that they can make full usage of powerful deep learning models.
When Bipasha Banerjee was looking for a Ph.D. program she had one major criteria: it
had to give the highest importance to research. With her continuing passion for
knowing more, she wanted to delve deeper into open questions and learn how to
“The quality of research in computer science at Virginia Tech is unparalleled and professors associated with the Sanghani Center are involved in projects that encompass a large range of real-world issues,” said Banerjee. “I realized this was the right fit for me and, thankfully I was accepted and started an exciting journey of research.”
In her Ph.D. research, Ola Karajeh is investigating efficient solutions to process social media such as Twitter for monitoring public health.
She is particularly interested in the brittleness of these systems, e.g., how non-informational tweets can lead to failure of public health monitoring systems. “Since many institutions report success from building supportive decision making systems based on data collected and processed from sources like Twitter, it is important to identify which posts are non-informational,” she said.
DeepOutbreak, a team of researchers from Virginia Tech, Georgia Tech, and the University of Iowa, has taken first place in the COVID-19 Symptom Data Challenge.
The competition explores how Facebook symptom survey data can enable earlier detection and improved situational awareness of COVID-19 and flu outbreaks that can help both public health authorities and the general public make better decisions.
The first place award, announced by Catalyst @Health 2.0 in late December, and the team’s work will be featured on the Facebook Data for Good blog. Facebook was one of the sponsors of the challenge. Click here to read more about the challenge.