Chelsea Finn is currently a research scientist at Google Brain and a post-doc at UC Berkeley, and will join the faculty at Stanford in 2019. She is interested in how learning algorithms can enable machines to develop more general notions of intelligence, allowing them to learn a variety of complex sensorimotor skills in real-world settings. During her PhD at UC Berkeley, she 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. She received her Bachelors degree in EECS at MIT. Her research has been recognized through an NSF graduate fellowship, a Facebook fellowship, and the C.V. Ramamoorthy Distinguished Research Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg.
For links to papers, videos, and open-sourced code and data, see: https://people.eecs.berkeley.edu/~cbfinn/