Wenlong Mou (牟文龙)

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PhD student,
Department of EECS,
UC Berkeley
Office: 264 Cory Hall, Berkeley, CA
E-mail: wmou.work [@] gmail [DOT] com
Google Scholar

About me

Welcome to my homepage! I am a final-year Ph.D. student at Department of EECS, UC Berkeley. I'm 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.

Starting from Fall 2023, I will join the Department of Statistical Sciences at University of Toronto as an Assistant Professor!


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


  • Mar 2023: New paper on debiased IPW estimator in high dimensions.

  • Jan 2023: New paper on instance-dependent optimality for off-policy estimation beyond semi-parametric efficiency bound.

  • Sep 2022: Paper on projected fixed-point accepted to Mathematics of Operations Research

  • Sep 2022: New paper on non-asymptotic theory for semi-parametric efficiency.

  • Sep 2022: Paper on non-smooth MCMC accepted to Journal of Machine Learning Research

  • Sep 2022: Paper on Markovian stochastic approximation selected as a finalist for INFORMS Applied Probability Society Best Student Paper competition