He is interested in how humans and other animals learn from experience, especially when faced with risky rewards or temporal uncertainty. To achieve this goal, his research builds and tests computational models of simple decision-making based on reinforcement learning. A key open question is how to scale up these simple algorithms to explain more complex cognitive processes, such as planning, imagination, and moral judgment. He is also always looking for new way to apply machine learning approaches to psychological data and theory. Prior to coming to Warwick, he completed his degree in Psychological and Brain Sciences at Duke University and did post-doctoral research in Neuroscience and Engineering at Princeton University and in Computing Science at the University of Alberta. Profile site