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
May 2026, Paper on RL with action-triggered observations accepted to ICML 2026.
Apr 2026, New paper on stochastic approximation with decision-dependent Markovian noise.
Feb 2026, New paper on test-time scaling laws.
Feb 2026, New paper on continuous-time reinforcement learning.
Nov 2025, Paper selected as an oral presentation at NeurIPS 2025 workshop on dynamics.
Oct 2025, Paper on continuous-time policy evaluation accepted to SIAM Journal on Mathematics of Data Science.
Oct 2025: New paper on RL with action-triggered observations.
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.