Xiao Wang

Xiao Wang is the Department Head and J.O. Berger and M.E. Bock Professor of Statistics at Purdue University's Department of Statistics. His research focuses on machine learning, nonparametric methods, and statistical inference with applications in engineering and physical sciences. He has advised numerous PhD students and holds multiple awards, including ASA and IMS fellowships.

Education: B.S. and M.S. from University of Science and Technology of China (1997–2000), Ph.D. in Statistics from University of Michigan (2005).

Research interests include deep learning, functional data analysis, and reliability theory. Recent work emphasizes multimodal sampling, generative adversarial networks, and high-dimensional regression.

Key awards: ASA/IMS Fellowships (2021), Purdue Professional Achievement Award, and William J. Studden Publication Award (2023). Active roles include associate editor of Technometrics and course instructor for advanced statistical theory courses.

Advised students have contributed to areas like robust adversarial learning, manifold approximation, and Bayesian neural networks. Current lab members include researchers in machine learning and computational statistics.