JoonHo Lee
Berkeley, California, United States
Summary
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SeniorJoonho Lee is a senior engineer and researcher with a Ph.D. in nonlinear control systems who has spent a decade applying advanced control theory and machine learning to autonomy problems across automotive, aerospace, and robotics domains. He has driven multi-time-scale deep reinforcement learning, stochastic approximation methods, and estimator design (including graph neural networks) at General Motors and now Boeing Research & Technology, combining model-based control (MPC, Kalman/GP-based filters) with modern learning approaches. His work spans under-actuated systems from quadrotors and helicopters to vehicle chassis and powered prosthetics, and he has a track record of making controllers robust to uncertainty and measurement noise. Based in Berkeley and a visiting scholar at UC Berkeley, he researches robotic world models and brings both rigorous academic training and practical production experience in R&D environments. An uncommon strength is his ability to bridge singular perturbation/multi-time-scale theory with hands-on implementation of perception and segmentation models for real-world autonomy.
9 years of coding experience
Github Skills (26)
pytorch10
tensorrt10
inference10
onnx10
coreml10
object-detection10
computer-vision10
deep-learning9
ultralytics9
convolutional-neural-networks7
classification7
cpp7
ros6
autonomous-driving5
dart5
Programming languages (7)
Github contributions (5)
JHLee0513/JHLee0513.github.io
Nov 2021 - Oct 2025
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Contributions:3 reviews, 11 PRs, 55 pushes in 3 years 11 months
data-developersciencedata-sciencejekyll-theme-minimaljekyll
JHLee0513/semantic_bevnet
Oct 2021 - Nov 2021
Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving (https://openreview.net/forum?id=AL4FPs84YdQ) (CoRL 2021)
Contributions:12 commits, 2 pushes, 1 branch in 9 days
autonomous-drivingclassification
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