Biography (Third-Person Narrative)

Tengyuan Liang is the JP Gan Professor of Econometrics and Statistics in the Wallman Society of Fellows at the University of Chicago Booth School of Business. His research focuses on the statistical and computational foundations of AI, as well as the reliable applications of AI in business and economics. He has published in leading journals in applied mathematics, economics, machine learning, and statistics.

His past work bridges the empirical and theoretical gap in statistical learning, develops optimization and inference procedures for over-parameterized models, and demystifies the role of stochasticity in non-convex optimization. He was awarded a National Science Foundation CAREER Grant for his contributions to modern statistical learning paradigms.

He served as an Associate Editor for the Journal of the American Statistical Association and the Operations Research, on the Editorial Board of the Journal of Machine Learning Research.

What I Study

I use insights and principles from learning theory and statistical theory to understand models and data. On the applied side, I study causal machine learning in business and economic contexts.

Currently, I am thinking about the following topics:

• probabilistic generative models

• experimental design and causal inference

• over-parametrization and regularization

• data visualization: patterns and uncertainties

Selected Past Work

Some selected publications in chronological order:

• T. Liang, A. Rakhlin. Just Interpolate: Kernel “Ridgeless” Regression Can Generalize.
  The Annals of Statistics, 48(3):1329-1347, 2020.

• M. H. Farrell, T. Liang, S. Misra. Deep Neural Networks for Estimation and Inference.
  Econometrica, 89(1):181-213, 2021.

• T. Liang. How Well Generative Adversarial Networks Learn Distributions.
  Journal of Machine Learning Research, 22(228):1-41, 2021.

• X. Dou, T. Liang. Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits.
  Journal of the American Statistical Association (Theory and Methods), 116:535, 1507-1520, 2021.

• T. Liang, P. Sur. A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers.
  The Annals of Statistics, 50(3):1669-1695, 2022.

• T. Liang. Universal Prediction Band via Semi-Definite Programming.
  Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84(4):1558–1580, 2022.

• T. Liang, B. Recht. Randomization Inference When N Equals One.
  Biometrika, forthcoming, 1-23, 2025.