I do research in machine learning and artificial intelligence, with frequent detours into the underlying fields of statistics, probability theory, and information theory. My Ph.D. supervisor is Csaba Szepesvári.

I belong to the Alberta Machine Intelligence Institute (AMII) and the Reinforcement Learning and Artificial Intelligence (RLAI) group.

See my list of publications or my curriculum vitae for more information.

Research Interests

I work on online learning and sequential decision making, where an agent learns incrementally as it continuously interacts with the world. In particular, I focus on bandit problems and reinforcement learning, both involving agents that learn to maximize a reward which tells them how effective their actions are.

I am currently working on reinforcement learning for complex environments with many states. I am trying to understand what makes such environments hard to plan in, and which simplifying assumptions are needed to make them tractable. I approach these questions through provably efficient algorithms as well as theoretical bounds on the hardness of planning in these environments.

Teaching

In Winter 2016, I was a principal instructor for “Introduction to Tangible Computing II” (CMPUT 275), an advanced introductory course for computing science. In 2014 and 2015 I was a teaching assistant for the same course.

This course combines labs and lectures in a “studio” format with three-hour sessions twice a week. Designed to liberate you from being a consumer of magic technology to a creator of it, it includes object-oriented programming, the Python programming language, and more complex algorithms and data structures such as shortest paths in graphs; caching, memoization, and dynamic programming; client-server style computing; recursion; and limited distribution of computation tasks between the Arduino platform and the traditional desktop in order to explore design tradeoffs.