I am an associate researcher in the Online Services and Advertising Group.  I am working on large scale recommender systems and computer Go.

I recently completed my PhD at David MacKay's Inference Group at the University of Cambridge. My thesis, 'Modelling Uncertainty in the Game of Go', presented a number of applications of machine learning to the game of Go. Go is an ancient Chinese game whose complexity has defeated attempts by Artificial Intelligence researchers to automate play. Typically in machine learning, uncertainty results from unpredictable aspects of the data which is often called 'noise'. In my work, I am primarily interested in uncertainty that results from a different source: limited computer speed (limited rationality). In Go, a board position in conjunction with the rules of the game contains all of the information necessary for perfect play. However, the sheer complexity of the game tree results in uncertainty about the future course of the game. I am interested in using probabilities (in the Bayesian sense) to represent and manage this uncertainty.