I am a Senior Research Scientist at Google DeepMind in NYC and Affiliate Faculty at the Center for Theoretical Neuroscience at Columbia University. My research spans topics in Neuroscience and Machine Learning. On the Neuroscience side, I study how animals build (and use) relational models of their world: similarities and differences between different experiences, predictive models of how events are likely to unfold, maps representing relationships between locations and objects in the real world or more abstract concepts. On the Machine Learning side, I work on implementing these cognitive functions in neural networks. I have worked particularly on Graph Neural Networks and Predictive models for simulation of complex physical systems. In 2019, was named one of MIT Tech Review’s Innovators under 35 for my work on predictive representations in hippocampus.
I completed my PhD at the Princeton Neuroscience Institute in Quantitative and Computational Neuroscience with Matthew Botvinick. Before that, I studied Mathematics (BA) and Chemical & Biological engineering (BS) at Tufts University.
Outside of research, I like art, especially drawing. I also like backpacking and canyoneering with friends. I practiced Martial Arts for many years and have a blackbelt in Isshinryu Karate and another in Tae Kwon Do.