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Data Science Faculty Fellowships

Data Science Faculty Fellowships were established in 2021 by an anonymous donor to enhance the national and international prominence of the study and teaching of data science at Virginia Tech.

The fellowships were established to recognize faculty dedicated to extraordinary research and teaching, to recruit scholars with exceptional records of achievement, and to retain high-performing faculty members with scholarly focus on data science or the application of data science within and across disciplines.

Recipients hold the title of Data Science Faculty Fellow for a period of two years.

2022-2023 Fellows

Xinwei Deng poses for a photo at Hutchinson Hall

Xinwei Deng poses for a photo at Hutchinson Hall

Xinwei Deng

A member of the Virginia Tech faculty since 2011, Xinwei Deng is a data science researcher working with both design of experiments and machine learning for large scale analysis, learning, and decision-making processes. His primary research focuses on developing theoretically sound and computationally efficient methods to model large, complex data; the interface between experimental design and machine learning; and novel statistical methods in emerging areas such as nanotechnology, tissue engineering, environmental science, risk analytics, and epidemiology. His research has practical implications for many important problems facing society.

A man standing in front of a wall of data drives

Quinn Thomas

Quinn Thomas has a bold vision: He wants us to predict the natural environment like we currently predict the weather.

“Imagine waking up to a 10-day forecast of the risk of interacting with ticks that have Lyme disease,” said Thomas, an associate professor of forest resources and environmental conservation in the College of Natural Resources and Environment and an associate professor of biology in the College of Science. “Imagine being able to forecast the carbon uptake that a forest is going to collect for a number of years or knowing which reservoirs a resource manager should pull water from and when. And then imagine doing all of that with shared tools, shared frameworks, and a shared computational infrastructure so that learning in one domain can easily transfer to another.”