His 80+ papers have presented many models and algorithms spanning numerous areas of NLP. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. In particular, he and his students introduced structured belief propagation, which integrates classical NLP models and their associated dynamic programming algorithms, as well as loss-calibrated training for use with belief propagation.