I'm a Ph.D. student in the NYU Center for Data Science and a NDSEG Fellow (class of 2018), working with Professor Andrew Wilson. My current research focuses on the incorporation of probabilistic state transition models in reinforcement learning algorithms. Model-based RL agents generalize from past experience very effectively, allowing the agent to evaluate policies with fewer environment interactions than their model-free counterparts. Improving the data-efficiency of RL agents is crucial for real-world applications in fields like robotics, logistics, and finance. I hold a Master’s degree in Operations Research from Cornell University, where I started working with Professor Wilson as a first-year Ph.D. student. I transferred from the Cornell doctoral program to continue my research agenda at NYU. Prior to my studies at Cornell, I earned a Bachelor’s degree in Mathematics from the University of Colorado Denver, graduating summa cum laude. In addition to my dissertation research, I'm interested in modern art and philosophy, especially epistemology and ethics. When not occupied with research, I enjoy volleyball, rock climbing, surfing, and snowboarding.