I lead the reinforcement learning efforts of the Google Research team in Montréal, Canada. I also supervise a number of graduate students at Mila, of which I am a core industry member.

My research focuses on two complementary problems in reinforcement learning. First comes the problem of representation: How should a learning system structure and update its knowledge about the environment it operates in? The second problem is concerned with exploration: How should the same learning system organize its decisions to be maximally effective at discovering its environment, and in particular acquiring information to build better representations?

From 2013 to 2017 I was at DeepMind in the United Kingdom. I completed my Ph.D. at the University of Alberta, focusing on the Atari 2600 as a benchmark for reinforcement learning research — what became the Arcade Learning Environment.