Ming received his PhD in Econometrics and Statistics at University of Chicago, Booth School of Business in March 2020. His research interests include high dimensional statistical inference, non-convex optimization, and reinforcement learning, with a focus on developing novel methodologies with both practical applications and theoretical guarantees.
Interests
- Machine Learning
- High-dimensional statistical inference
- Nonconvex optimization
- Reinforcement learning
- Network models and information diffusion
Education
PhD in Econometrics and Statistics, 2020
University of Chicago Booth School of Business
MS in Statistics, 2016
University of Chicago
BS in Mathematics, 2014
Peking University