Xinwei Deng

Xinwei Deng is a Professor of Statistics and Data Science Faculty Fellow at Virginia Tech, affiliated with the Department of Statistics in the College of Science. He also co-directs the VT Statistics and Artificial Intelligence Laboratory (VT-SAIL). He earned his Ph.D. from Georgia Institute of Technology in 2009 under Professors C.F. Jeff Wu and Ming Yuan, and joined Virginia Tech in 2011.

Education: PhD in Statistics (Georgia Tech, 2009), B.S. in Mathematics (Nanjing University, China, 2003).

Research Interests: Focuses on the interface between experimental design and machine learning, uncertainty quantification, covariance matrix estimation, high-dimensional data analysis, and applications in nanotechnology, manufacturing, and healthcare. His work bridges statistical theory with practical challenges in emerging technologies like tissue engineering and environmental science.

Publications: Over 80 peer-reviewed articles in top journals such as Journal of the American Statistical Association, Technometrics, and Proceedings of the National Academy of Sciences. Recent work emphasizes scalable algorithms, AI resilience in manufacturing, and uncertainty quantification in complex systems.

Awards/Honors: Includes Data Science Faculty Fellowships, ISI election (2017), and multiple best paper awards. Recognized for contributions to statistical methodologies in quality engineering and AI robustness.

Teaching & Advising: Teaches advanced courses like Data Analytics, Experimental Design, and Multivariate Analysis. Mentors students in interdisciplinary projects spanning statistics, computer science, and engineering.

Labs/Teams: Leads VT-SAIL, fostering AI-driven statistical solutions for real-world problems. Collaborates with industry partners (e.g., P&G, Deloitte) and government agencies (NSF, AFRL) on applied research.