Wenlong Mou (牟文龙)
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Assistant Professor, |
About me
Welcome to my homepage! I am an Assistant Professor at the Department of Statistical Sciences, University of Toronto. I recently obtained my Ph.D. from the Department of EECS, UC Berkeley, where I was very fortunate to be advised by Prof. Martin Wainwright and Prof. Peter Bartlett. Prior to Berkeley, I received B.S. in Computer Science from Peking University in 2017, where I was very fortunate to work with Prof. Liwei Wang.
Research
My research interests are broadly in statistics, machine learning theory, dynamic programming and optimization, and applied probability. In particular, I develop optimal statistical methods that enable optimal decision making, powered with efficient computational algorithms. Currently, I work on the following topics:
Theory of reinforcement learning with function approximation, geometry of Bellman equations, statistical complexity of reinforcement learning
Non-asymptotic theory of semi-parametric statistics, finite-sample optimal methods for treatment effect estimation
Stochastic approximation algorithms under statistical settings, interplay between geometry, stochastic processes and optimization
Discretization of diffusion processes, high-dimensional MCMC algorithms, Bayesian posterior contraction
Recruiting
I am actively seeking Ph.D. students. Please see the post for detailed information. If you are interested in collaborating with me, please don't hesitate to reach out. However, please be aware that I receive a high volume of emails daily, which may lead to some delays in my response. Your patience in this regard is greatly appreciated. Thank you.
News
Nov 2024: New paper on optimal interpolation between bootstrap and rollout methods in reinforcement learning.
Nov 2024: New paper on debiasing general Z estimators.
July 2024: New paper on policy evaluation for continuous-time reinforcement learning.
Dec 2023: Paper on diffusion process methods for Bayesian posterior contraction accepted to SIAM Journal on Mathematics of Data Science
Nov 2023: New paper on general and near-optimal methods for regression adjustment in finite population framework.
Sep 2023: Paper on Markovian stochastic approximation accepted to Mathematical Statistics and Learning