Marc Lanctot is a post-doctoral research fellow at Maastricht University whose research interests are in the area of Monte Carlo sampling algorithms for game tree search and equilibrium computation.
Marc holds B.Sc. and M.Sc. degrees from McGill University and Ph.D. from University of Alberta. In his Ph.D., Marc worked on Monte Carlo Counterfactual Regret Minimization (MCCFR), an algorithm that uses sampling to compute approximate equilibria for large imperfect information games. Recently, he has become interested in Monte Carlo sampling methods for game tree search. In particular, he is interested in models and techniques that can be used to gain a deeper understanding of the Monte Carlo Tree Search (MCTS) algorithm.
Phone: +31 43 38 82005
Fax: +31 43 38 84910
Personal page: http://mlanctot.info