Extensive experience in developing novel physics-inspired theories and tools, including but not limited to: collaborating with experimental biologists to build phenomenological models of learning and decision-making; using machine learning as 'experimental systems' to guide the development of new theory.
Background
Research interests include understanding how living and artificial systems learn and solve goal-oriented tasks; areas of expertise span animal behavior, neuroscience, evolution, and machine learning.
Miscellany
Passionate about training a new generation of scientists who have both a strong theoretical foundation rooted in physics and an appreciation for biological complexity.