My goal is to understand and improve the algorithms that researchers can use to learn about the structure of an underlying system from data and reason about intervening in such a system.

Structure learning and hypothesis testing -- What we assume about the underlying causal structure affects how we understand data and the association between variables. I am interested in learning from data alone the invariant features of the underlying causal graph, with a particular interest in problems involve complex data types. In recent work, we showed how to learn local independence graphs from irregularly-sampled time series data.