Leads the Machine Intelligence through Decision-making and Interaction (MIDI) Lab, focusing on theory and algorithms for sequential decision-making problems, with an emphasis on reinforcement learning, self-supervised learning, and representation learning, aiming to improve robustness, generalization, and sample efficiency.
Education
PhD from McGill University and Mila - Quebec AI Institute, co-supervised by Joelle Pineau and Doina Precup; M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT.
Background
Assistant professor at UT Austin. Works on state abstractions and generalization in reinforcement learning.