Mohammad Ghavamzadeh received a Ph.D. degree in computer science from the University of Massachusetts Amherst in 2005. Since September 2005 he has been a postdoctoral fellow at the Department of Computing Science at the University of Alberta, working with Prof. Richard Sutton. The main objective of his research is to investigate the principles of scalable decision-making grounded by real-world applications. In the last two years, Ghavamzadeh’s research has been mostly focused on using recent advances in statistical machine learning, especially Bayesian reasoning and kernel methods, to develop more scalable reinforcement learning algorithms.