I study the computational and neural processes that humans use to filter and distill all of the complexity in their social world into adaptive social inferences. Using a computational psychiatry approach, my research also examines how these social learning and inference mechanisms diverge in people with anxiety and depression who are particularly impacted by social information and uncertainty. My research goals are to 1) neurally map the cognitive and computational mechanisms of social learning, 2) identify how these social learning pathways are altered in people with mood disorders and 3) explore how computational phenotyping with social task batteries and neural data can help match psychiatric patients with treatment plans. To investigate this space, my research combines economic decision-making tasks with fMRI, eyetracking, pupillometry, Reinforcement Learning, and Bayesian learning models.

In my off time, I’m thinking and writing about our relationship with AI and technology which is increasingly shaping our social landscape and mental health.