His work pertains to inferential, algorithmic and estimation questions in machine learning, including large scale probabilistic distributed inference, deep learning, and causal inference. The applied side of his work has focused on problems in natural language processing such as parsing, regulatory models in computational biology, computational chemistry, and computational functional genomics. He received the Sloan Research Fellowship 2002 and many awards for his publications, across areas.