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Academic
I received my BMath Honours Computer Science, Co-op from the University of Waterloo in 2005. In 2007 I received my MMath Computer Science, also from the University of Waterloo.
My main areas of interest are multiagent learning and game theory.
My Masters focused on trying to understand how agents could achieve equilibrium in a repeated game. I just started my PhD so I haven't had much time to think about new research. However, I am interested in examining alternatives to Nash equilibria. Specifically, any solutions to a repeated game can be judged by two factors; rationality and utility. Nash equilibria fail on both accounts; they often result in low utility and while it makes sense to keep playing a Nash equilibrium once it has been reached, there is no reason to reach one in the first place. There have been many attempts at alternatives. Some of these include the "AI agenda" and correlated equilbira. In my opinion, all of these proposed solutions fail to pay enough attention to at least one of these metrics.
My CV is here.
Personal
I am an avid long distance runner. I recently got into trail running and ran my first half-marathon (actually 25k) earlier this year. I have also just gotten into mountain biking and am taking swimming lessons.
When I'm not sweating, I'm probably either listening to music, goofing off or watching the Daily Show or the Colbert Report.