Chelsea Finn is an Assistant Professor of Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has pioneered end-to-end deep learning methods for vision-based robotic manipulation, meta-learning algorithms for few-shot learning, and approaches for scaling robot learning to broad datasets. Her research has been recognized by awards such as the Sloan Fellowship, the NSF CAREER Award, and the ACM doctoral dissertation award, and has been covered by various media outlets including the New York Times, Wired, and Bloomberg. Prior to Stanford, she received her Bachelor’s degree in EECS at MIT and her PhD in CS at UC Berkeley.

AI2050 Project

Machine learning models are known to be brittle when deployed in the real world, when correlations that exist in the training data do not hold in new scenarios. In contrast, humans can learn concepts efficiently and robustly by having a teacher correct their mistakes. Chelsea Finn hypothesizes that machine learning models can similarly learn robust functions by combining the training data with corrective information that targets the model’s underlying “misconceptions.” This project’s goal is to develop a new framework that seeks out small amounts of targeted information about a model’s misconceptions and uses that interaction to robustify the model.