I’m a PhD student with the NYU Center for Data Science, advised by Brenden Lake and Todd Gureckis. My research interests center around cognitively-inspired machine learning: how can we draw inspiration from human cognition to advance the design of machine learning methods. I am particularly interested in studying the compositional space of tasks humans operate in and using cognitively-driven task representations to improve exploration in reinforcement learning. I am also very excited about the role of objects in reinforcement learning and object-centric reasoning.

In my non-academic life, I enjoy playing ultimate frisbee, making homemmade fermented hot sauces, and making friends with all of the puppies in Brooklyn.