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 focuses on mathematics of machine learning in the era of large AI models. In particular, I work on the following topics:
Post-training optimization of generative models. Reinforcement learning fine-tuning and test-time adaptation.
Practical structures that enables efficient reinforcement learning with function approximation. RL in continuous-time diffusion processes.
Stochastic approximation for large-scale machine learning.
Incorporation of machine learning into causal and semiparametric estimation problems.
On the applied side, I am interested in various applications of machine learning for engineering problems in the physical world.
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
Sep 2025: New paper on RL fine tuning of diffusion models with function approximation.
Sep 2025: New paper on federated learning with physical communication channels.
Feb 2025: New paper on statistical guarantees for continuous-time reinforcement learning
Nov 2024: New paper on optimal interpolation between bootstrap and rollout methods in reinforcement learning.
Nov 2024: New paper on debiasing general Z estimators.