My research interests center around designing flexible and robust algorithms for embodied AI. Currently, I’m investigating intrinsic motivation objectives for causal, model-based RL. I’m also working to improve multi-task inverse RL with temporal contrastive learning, aiming to replace expert demonstrations with self-supervised exploration. Previously, I completed my Master’s thesis on designing spatial birth-death point processes to model decentralized morphogenesis. I received my M.S.E. from Princeton University and my A.B. from Harvard University, both in computer science.