| Chelsea Finn | |
| Workplaces: | Stanford University |
| Alma Mater: | University of California, Berkeley Massachusetts Institute of Technology |
| Thesis Title: | Learning to Learn with Gradients |
| Thesis Url: | http://www.worldcat.org/oclc/1083628768 |
| Thesis Year: | 2018 |
| Doctoral Advisor: | Sergey Levine Pieter Abbeel |
| Known For: | Deep reinforcement learning |
| Website: | IRIS LAB |
Chelsea Finn is an American computer scientist and assistant professor at Stanford University. Her research investigates intelligence through the interactions of robots, with the hope to create robotic systems that can learn how to learn. She is part of the Google Brain group.
Finn was an undergraduate student in electrical engineering and computer science at Massachusetts Institute of Technology. She then moved to the University of California, Berkeley, where she earned her Ph.D. in 2018 under Pieter Abbeel and Sergey Levine. Her work in the Berkeley Artificial Intelligence Lab (BAIR) focused on gradient based algorithms .[1] Such algorithms allow machines to 'learn to learn', more akin to human learning than traditional machine learning systems.[2] [3] These “meta-learning” techniques train machines to quickly adapt, such that when they encounter new scenarios they can learn quickly.[4] As a doctoral student she worked as an intern at Google Brain, where she worked on robot learning algorithms from deep predictive models. She delivered a massive open online course on deep reinforcement learning.[5] [6] She was the first woman to win the C.V. & Daulat Ramamoorthy Distinguished Research Award.[7]
Finn investigates the capabilities of robots to develop intelligence through learning and interaction.[8] She has made use of deep learning algorithms to simultaneously learn visual perception and control robotic skills.
She developed meta-learning approaches to train neural networks to take in student code and output useful feedback.[9] She showed that the system could quickly adapt without too much input from the instructor. She trialled the programme on Code in Place, a 12,000 student course delivered by Stanford University every year. She found that 97.9% of the time the students agreed with the feedback being given.[10]