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.
Jia’s research interest broadly spans the areas of machine learning, security, privacy, and cyber-physical systems. Her recent work focuses on building algorithmic foundations for data markets and developing trustworthy machine learning solutions. Towards that end, she and her group work on a range of projects, including data valuation and quality management, data privacy, active data acquisition, adversarial machine learning, and explainable machine learning.
Eldardiry, who earned a
master’s (2006) and PhD (2012) in computer science, in addition to an MBA
(2011), was recognized for “enormous contributions to artificial intelligence
and the computing research community at large.” Her research on
artificial intelligence has led to the development of AI and machine learning
that tackle complicated modern issues such as fraudulent medical claims, cyber
threat protection, and sensors that can assist with panic attacks.
She has
published over 35 peer-reviewed papers and holds 19 patents. She has managed
research projects for government and commercial sectors with her own share of
the awards exceeding $12 million.
Her other honors
include the Creative Young Engineer Award and the Xerox Innovation Group
Research Recognition Award for Advancing the Edge of Innovation.
Determined to support
tomorrow’s future scientists, Eldardiry has also served as a professional
mentor for the U.S. State Department TechWomen Program, a Bureau of
Educational and Cultural Affairs initiative to combine global business,
technology, and education power.
Graphic is from the paper “The Case For Alternative Web Archival Formats To Expedite The Data-To-Insight Cycle”
Xinyue
Wang was an undergraduate research assistant involved in artificial
intelligence and digital library research at the University of North Texas when
he had occasion to connect with Edward Fox, professor in Virginia Tech’s Department of Computer Science and faculty
at the Sanghani Center and Zhiwu Xie, a professor at University
Libraries.
The two are now Wang’s co-advisors as he pursues a Ph.D. in computer science at Virginia Tech. “They are wonderful people and I am grateful to be able to learn from them and work with them,” he said.
Research associates Brian Mayer (top) and Nathan Self (bottom) meet virtually to review targeted violence events on the dashboard developed by the Sanghani Center.
About the series: Every complex problem has many multidisciplinary angles. Leveraging expertise and energy, Virginia Tech faculty and students serve humanity by addressing the world’s most difficult problems.
With risk of political and targeted violence on the rise across the United States, national and local leaders are asking Princeton University’s nonpartisan Bridging Divides Initiative (BDI) to provide them with more timely, reliable, and context-specific data on targeted violence events that could help them engage locally and better inform their policy decisions.
As part of their response to this plea, BDI’s team of Princeton social scientists collaborated with data scientists at the Sanghani Center for Artificial Intelligence and Data Analytics to identify targeted violence events. These often include hate crimes and other incidents that target individuals because of their race, religion, sexual orientation, or other perceived characteristics. Click here to read more about this research.
Lourentzou most recently served as a research scientist at IBM Almaden Research Center where she worked on machine learning, natural language processing, and information retrieval problems. In 2019, she was recognized with an IBM Invention Achievement Award and was selected to participate in Rising Stars in EECS.
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.
Graphic is from the paper “Category-Level Articulated Object Pose Estimation.”
Xiaolong Li is a Ph.D. student in computer engineering. His main interest is in computer vision, with a focus on deep 3D representations learning for dynamic scene understanding.
“Building robust smart algorithms will help machines understand the 3D world around us,” Li said.
Graphic is from the paper “A Specification Language for Matching Mistake Patterns with Feedback.”
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.
Graphic is from the paper “Narrative Maps: An Algorithmic Approach to Represent and Extract Information Narratives”
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.
Yali Bian, Ph.D. student in the Department of Computer Science Graphic from the paper “DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning”
According to Yali Bian,
the Sanghani
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.
