His personal research interests include behavioral modeling and learning to act/behave (reinforcement learning), scalability, representation, and the tradeoff between stochastic and deterministic modeling. All of these represent different facets of his overall interest in scaling learning methods to large, complex spaces and using them to learn to perform lengthy, complicated tasks and to generalize over behaviors. While he attempts to understand the core learning issues involved, he often situates his work in domain studies in practical problems. Doing so both elucidates important issues and problems for the learning community and provides useful techniques to other disciplines.