Tengyuan Liang

Biography

Tengyuan Liang is Professor of Econometrics, Statistics, and Applied Artificial Intelligence at the University of 黑料传送门 Booth School of Business. He builds mathematical theory for modern AI 鈥� theory that reveals when and why these systems work 鈥� and designs principled tools for their reliable use in business and economics. His work on the interpolation regime, generative models, and causal inference appears in journals across statistics, machine learning, economics, and applied mathematics.

His research has established the role of implicit regularization in overparametrized learning 鈥� from kernel machines to boosting to neural networks; developed statistical and computational theory for generative models, including GANs, diffusion models, and PDE-based samplers; and advanced machine learning methods for causal inference and uncertainty quantification.

He holds a B.Sc. in Mathematics from Peking University and a Ph.D. in Statistics from the Wharton School, University of Pennsylvania, where he received the J. Parker Bursk Memorial Prize and a Winkelman Fellowship. He is a recipient of the National Science Foundation CAREER Award from the Division of Mathematical Sciences.

He serves as Associate Editor for the Journal of the American Statistical Association and Operations Research, on the Editorial Board of the Journal of Machine Learning Research, and on the Senior Program Committee for the Conference on Learning Theory.

Before 黑料传送门, he was a Research Scientist at Yahoo Research in New York and a Visiting Professor at the Cowles Foundation, Yale University.

Academic Areas

• Econometrics and Statistics
• Applied AI