Researches

My research investigates how agents with different information can communicate and cooperate. It connects information-theoretic foundations of distributed inference with pragmatic models of retrieval and dialogue. A central theme is that communication should be understood through its contribution to a shared task: what an agent communicates, what another agent can infer, and how the exchange changes subsequent decisions.

My research investigates the mathematical principles underlying learning and representation. Drawing on information theory and statistical decision theory, I study how representations retain task-relevant information, how their usefulness can be compared, and how knowledge can be transferred across models.

My research studies how intelligent systems can recognize uncertainty, identify potential failures, and support decisions about when to act, reject a prediction, or seek additional evidence. This work connects statistical measures of uncertainty with practical evaluation methods.