Machine Learning, Probability Modeling, Optimization, Search.
My long term goal is to develop systems that learn predictive models from massive data sources when the requisite models are complex (e.g., as in perception, language interpretation, information extraction, bio-informatics, robot learning). Some of the key challenges are knowledge representation for learning -- how to usefully express and debug prior domain assumptions -- and navigating complex model spaces -- how to find good models while avoiding over/under-fitting. Some ongoing projects include: statistical natural language modeling, reinforcement learning, and learning search control. I've also developed some new methods for probabilistic inference, optimization, and constraint satisfaction.