I studied Physics and Brain & Cognitive Sciences as an undergraduate at MIT, and completed my PhD at Princeton Neuroscience. During my PhD, I investigated the algorithms that the brain uses for memory and for decision making/reinforcement learning, using a combination of computational modeling, psychological experiments, and fMRI data analysis. In particular, I studied how the brain represents the state of the world for reinforcement learning and for organizing its memories. After that, I led a research team at a startup in Boston/Barcelona, developing technologies for detecting emotions through computer vision and biophysical signals. Then I worked briefly as lead machine learning engineer at an e-commerce startup in Hong Kong. Here at the Residency, I have so far been working on measuring and improving the replicability of reinforcement learning algorithms. I find the atmosphere here to be stimulating, supportive, and productive. There is a very nice complementary mix, in that we are given the intellectual freedom to own and direct our work, while still receiving a great deal of support from the program, our mentors, other AI Residents, and other people at Google more generally. I also love the flow of ideas and abundant conversation between the many different parts of the research organization.