Statistical Relational Learning (SRL), studies techniques that combine the strengths of relational learning (e.g. inductive logic programming) and probabilistic learning (e.g. Bayesian networks). By combining the power of logic and probability, such systems can perform robust and accurate reasoning and learning about complex relational data. See the book: Introduction to Statistical Relational Learning. Our work in the area has primarily focused on applications of SRL methods to problems in natural language processing, transfer learning, and abductive reasoning.