Chelsea Finn (born October 8, 1992) is an American computer scientist and assistant professor at Stanford University. Her research investigates how robots can develop intelligence through interaction, aiming to create robotic systems that can 'learn to learn.' She previously worked at Google and is a co-founder of the startup Physical Intelligence.
Finn's work focuses on meta-learning, a subfield of Machine learning that trains algorithms to adapt quickly to new tasks, more akin to human learning than traditional systems. She has made significant contributions to Deep learning and Robotics, particularly in developing algorithms that enable robots to learn from visual perception and control simultaneously.
Early life and education
Finn studied electrical engineering and computer science as an undergraduate at the Massachusetts Institute of Technology. She then moved to the University of California, Berkeley, where she earned her Ph.D. in 2018 under advisors Pieter Abbeel and Sergey Levine. Her doctoral research, conducted at the Berkeley AI Research lab, focused on gradient-based meta-learning algorithms. These techniques allow machines to 'learn to learn,' enabling rapid adaptation when encountering new scenarios.
During her doctoral studies, Finn interned at Google Brain, where she worked on robot learning algorithms using deep predictive models. She also delivered a massive open online course on deep reinforcement learning. In 2016, she became the first woman to win the C.V. & Daulat Ramamoorthy Distinguished Research Award.
Research and career
Finn investigates the capabilities of robots to develop intelligence through learning and interaction. She has used deep learning algorithms to simultaneously learn visual perception and control robotic skills, advancing the field of artificial intelligence. Her work has implications for neural networks and reinforcement learning applications.
One notable project involved developing meta-learning approaches to train neural networks to provide feedback on student code. She demonstrated that the system could quickly adapt with minimal instructor input. In a trial on Code in Place, a 12,000-student course delivered annually by Stanford University, students agreed with the feedback 97.9% of the time.
Finn's research has been influential in the Robotics community, and she is recognized as a leading figure in meta-learning. She is also a co-founder of Physical Intelligence, a startup focused on bringing intelligent robotics to real-world applications.
Awards and honors
Finn has received numerous awards for her contributions to computer science and robotics. These include:
- 2016: C.V. & Daulat Ramamoorthy Distinguished Research Award
- 2017: Electrical Engineering and Computer Science Rising Star
- 2018: MIT Technology Review 35 Under 35
- 2018: ACM Doctoral Dissertation Award
- 2020: Samsung Advanced Institute of Technology AI Researcher of the Year
- 2020: Intel Rising Star Faculty Award
- 2021: Office of Naval Research Young Investigator Award
- 2022: IEEE Robotics and Automation Society Early Academic Career Award
Selected publications
Finn has authored several influential papers in machine learning and robotics. Notable works include:
- Finn, Chelsea; Abbeel, Pieter; Levine, Sergey (2017). 'Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.' International Conference on Machine Learning.
- Levine, Sergey; Finn, Chelsea; Darrell, Trevor; Abbeel, Pieter (2016). 'End-to-End Training of Deep Visuomotor Policies.' Journal of Machine Learning Research.
- Finn, Chelsea; Goodfellow, Ian; Levine, Sergey (2016). 'Unsupervised Learning for Physical Interaction through Video Prediction.' Advances in Neural Information Processing Systems.
These publications have been widely cited and have shaped research in meta-learning and robot learning.
References
Finn's work is documented in academic literature and her Stanford University profile. Her contributions to artificial intelligence continue to influence both academic research and industry applications.