Rate My Professor Laurent Charlin

Charlin's research focuses on machine learning and artificial intelligence, particularly recommender systems, deep learning, Bayesian statistics, reinforcement learning, and applications in traffic signal control, energy management, and decision-making under uncertainty. His work has over 11,900 citations according to Google Scholar. Key publications include 'IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control' (IEEE Transactions on Intelligent Transportation Systems, 2022), 'Dynamic Poisson Factorization' (ACM RecSys, 2015), 'Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control' (IEEE Open Journal of Intelligent Transportation Systems, 2024), and 'Towards sustainable energy use: Reinforcement learning for demand response in commercial buildings' (Energy and Buildings, 2025). He co-developed the Toronto Paper Matching System (TPMS), adopted by major machine learning conferences to match thousands of reviewers to papers. Charlin has supervised multiple PhD dissertations and master's theses, such as those on inductive graph reinforcement learning for traffic control and prosumer-distributor dynamics in energy resources. He serves as Editor-in-Chief of Transactions on Machine Learning Research (TMLR), has been on Mila's Scientific Council since 2017, and has received honors including runner-up best paper at UAI 2008, Google Focused Research Award (2017), and various graduate scholarships like the Alexander Graham Bell Canada Graduate Scholarship (2009–2011). He engages in knowledge transfer through MOOCs, introductory talks, and media interviews.