Katie's research focuses on perception for self-driving, with an end goal of bringing self-driving to a diverse set of end users. While machine learning has shown great advances in a variety of fields such as computer vision, the current paradigm for self-driving trains the perception systems on specific environments but then deploys them to end-users into a diverse set of vehicle behavior and appearances. This change in environment makes it hard to guarantee high accuracy outside of the development laboratory. Katie's work focuses on exploring additional channels of information to adapt to diverse, real-world scenarios in a data efficient manner.
Katie is a Ph.D. student at Cornell University, advised by Prof. Kilian Q. Weinberger. Her research interests mostly lie in machine learning and computer vision for autonomous driving. Prior to her Ph.D., Katie was an AI Resident at Uber ATG (now part of Aurora), and she received a B.Sc. and M.S. in Electrical Engineering and Computer Science from the University of California, Berkeley.