Overview

I am a Ph.D. candidate generally interested in AI, machine learning, and games. I would like to know how intelligent beings make decisions, how they represent information and reason about it efficiently, and how interaction can lead to knowledge gain.


Decision-making is an inherent component of game-playing. Most games are easy to observe; each instance is easy to record and learn from. People like playing games so there certainly is a wealth of data to learn from. In addition, games are often played quite competitively; the decisions made by the players usually reflect profoundly considered thoughts and conclusions. Therefore, game-playing is a perfect context from which to study intelligent decision-making.


I am particularly interested in computational game theory and learning through self-play. These days I am working on regret minimization methods for solving large extensive-form games. While my work focuses on imperfect information games, it is not tied to a specific game; I am interested in the behavior of these algorithms in the general case, as a function of the type of imperfect information, or as a function of the structure of the game.


If you would like to know more feel free to contact me about anything, either by email or dropping by the AI lab.


Schedule

I am now living in Toronto and so will be commuting to Edmonton every second Thursday. I normally arrive on campus at 12-12:30. If I stay overnight, I leave some time Friday afternoon. Otherwise, I get the last flight on Thursday evening. To catch the latest flight I need to leave campus by 4:15 to get the last Sky Shuttle at 4:30.

I no longer maintain a schedule of when I'm in Edmonton. If you want to meet with me, please email me.

Quote of the day

C makes it easy to shoot yourself in the foot; C++ makes it harder,
but when you do, it blows your whole leg off.
  -- Bjarne Stroustrup

Last Modified: Jul 15th, 2009

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