Kuno Kim
PhD Candidate
Palo Alto, California, United States
Summary
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RockstarKuno Kim is a Research Scientist at Tesla and a PhD-trained researcher from Stanford ( advised by Stefano Ermon) with eight years of experience developing scalable algorithms for learning-based agents. He works at the intersection of imitation learning, reinforcement learning, and inverse reinforcement learning, combining theory — including identifiability theorems for MDPs — with practical advances that have driven state-of-the-art IRL applications in robotics and econometrics. Kuno’s approach borrows tools from statistics, computer vision, generative and sequence modeling, which enabled unified algorithms for imitation under domain mismatch. Recently he has explored language-model-driven skill abstractions and fractal image compression, and now applies that expertise to training end-to-end models for Tesla’s self-driving stack.
8 years of coding experience
Github Skills (4)
probabilistic8
graphical-models8
probabilistic-graphical-models7
machine-learning5
Programming languages (1)
Github contributions (5)
khkim1/khkim1.github.io
Oct 2017 - Apr 2023
Contributions:71 pushes, 1 branch in 5 years 6 months
ermongroup/dail
Oct 2020 - Oct 2020
Contributions:10 commits, 14 pushes, 3 branches in 1 day
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