Chelsea Finn
cbfinn at cs dot stanford dot edu

I am an Assistant Professor in Computer Science and Electrical Engineering at Stanford University and co-founder of Pi. My lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the ML Group.

I am interested in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction.

Previously, I completed my Ph.D. in computer science at UC Berkeley and my B.S. in electrical engineering and computer science at MIT. I also spent time at Google as part of the Google Brain team.

Prospective students and post-docs, please see this page.

CV  /  Bio  /  PhD Thesis  /  Google Scholar  /  Twitter  /  IRIS Lab


See our lab website for up-to-date news.

Recent Talks

Robotics Focused Talk (November 2022)

Machine Learning Focused Talk (June 2022)
Students and Post-Docs

See this page for a list of lab members.


Stanford CS224R: Deep Reinforcement Learning - Spring 2023
Stanford CS330: Deep Multi-Task and Meta Learning - Fall 2019, Fall 2020, Fall 2021, Fall 2022
Stanford CS221: Artificial Intelligence: Principles and Techniques - Spring 2020, Spring 2021
UCB CS294-112: Deep Reinforcement Learning - Spring 2017

Tutorials and Lectures
  • Lecture videos for the Fall 2021 edition of CS330 are available online here.
  • In Fall 2019, I taught a new course on deep multi-task and meta learning. Lecture videos are available here.
  • At ICML 2019 and CVPR 2019, I gave an invited tutorial on Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning. Slides, video, and references are linked here.
  • In December 2018, I gave a tutorial on model-based reinforcement learning at the CIFAR LMB program meeting (slides here).
  • At ICML 2017, I gave a tutorial with Sergey Levine on Deep Reinforcement Learning, Decision Making, and Control (slides here, video here).
  • In August 2017, I gave guest lectures on model-based reinforcement learning and inverse reinforcement learning at the Deep RL Bootcamp (slides here and here, videos here and here).
  • In Spring 2017, I co-taught a course on deep reinforcement learning at UC Berkeley. All lecture video and slides are available here.

This guy makes a nice webpage.