Keynote by Chelsea Finn: Training for Generalization

TBA.

Bio: Chelsea Finn is a research scientist at Google Brain, a post-doc at Berkeley AI Research Lab (BAIR), and will join the Stanford Computer Science faculty in Fall 2019. Finn’s research studies how new algorithms can enable machines to acquire intelligent behavior through learning and interaction, allowing them to perform a variety of complex sensorimotor skills in real-world settings. She has developed deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for scalable acquisition of nonlinear reward functions, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn’s research has been recognized through an NSF graduate fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the Technology Review 35 Under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. With Sergey Levine and John Schulman, she also designed and taught a course on deep reinforcement learning, with thousands of followers online.

Finn received a PhD in Computer Science from UC Berkeley and a S.B. in Electrical Engineering and Computer Science from MIT.