Chelsea Finn
Co-Founder at Physical Intelligence
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChelsea Finn is a research-driven entrepreneur and co-founder with 14 years of experience at the intersection of machine learning, robotics, and software engineering. As a former Google Brain research scientist and current Stanford assistant professor, she has advanced meta-learning and robotic control algorithms—contributions visible in widely used repos like maml and guided policy search where she implemented core training logic and practical bug fixes for real-world simulators. Her work spans hands-on systems engineering (from Mujoco/Box2D fixes to data preprocessing for mini-ImageNet) to building startups focused on embodied intelligence, combining academic rigor with product-minded execution. Based in Palo Alto, she holds a PhD from UC Berkeley and a BS from MIT, and is known for translating cutting-edge research into robust, reproducible code and tangible robotic systems.
14 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD) EECS, Doctor of Philosophy (PhD) EECS at University of California, BerkeleyBS Electrical Engineering and Computer Science, BS Electrical Engineering and Computer Science at Massachusetts Institute of TechnologyAmador Valley High School
Stackoverflow
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0reached
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Github Skills (17)
algorithm10
code-optimization10
algorithms10
python10
machine-learning10
tensorflow10
optimisation10
numerical-optimization10
optimization10
reinforcement-learning9
gymnasium9
openai-gym9
mujoco9
hyperparameter-tuning8
data-processing8
Programming languages (3)
Github contributions (5)
cbfinn/maml
Jun 2017 - Oct 2019
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
Role in this project:
ML Engineer
ML EngineerContributions:41 commits, 7 PRs, 24 pushes in 2 years 4 months
Contributions summary:Chelsea primarily worked on the core MAML (Model-Agnostic Meta-Learning) implementation, making adjustments to the main training script and data processing. Their contributions included removing a baseline, fixing a bug in testing, and updating usage instructions to match the paper's specifications. They also made minor bug fixes in the image processing scripts for the mini-imagenet dataset, demonstrating involvement in data preparation.
pytorchautoencoderdeep-networksadversarial-learningmeta-learning
berkeleydeeprlcourse/homework
Jan 2017 - Aug 2017
Assignments for CS294-112.
Role in this project:ML Engineer
ML EngineerContributions:11 commits, 1 PR, 5 pushes in 6 months
Contributions summary:Chelsea contributed to the project by adding and modifying files related to the expert policy and behavioral cloning. The user modified the run\_expert.py file to use the expert policy. Also added the humanoid expert to the expert's directory. The user tweaked the file with bug fixes. The user also updated the usage documentation for the expert policy execution file.
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