Dongchan Lee
Applied Scientist II at Amazon Fulfillment Technologies & Robotics
Cambridge, Massachusetts, United States
Summary
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Senior🎓
Top SchoolDongchan Lee is an Applied Scientist II based in Cambridge, MA, with eight years of experience at the intersection of robotics, controls, and machine learning. Currently developing high-fidelity robotic simulation software in C++ and Python at Amazon Fulfillment Technologies & Robotics, he brings deep expertise in robotic manipulation and simulation-driven development. His PhD research at MIT focused on robust optimization and model predictive control, and he has taught and contributed to the well-known underactuated robotics course material used at MIT and edX. Dongchan has a strong track record of practical ML engineering, including hands-on contributions to the underactuated repository (extending ML exercises and system identification content) and industry experience in vehicle control at TuSimple. He blends academic rigor from MIT and University of Toronto with production-minded software engineering, making him adept at turning theoretical control and robustness ideas into scalable, testable systems. An understated strength is his habit of improving educational tooling and exercises, reflecting a commitment to reproducible, learnable engineering.
8 years of coding experience
8 years of employment as a software developer
PhD, Mechanical Engineering, PhD, Mechanical Engineering at Massachusetts Institute of TechnologyMASc, Electrical and Computer Engineering, MASc, Electrical and Computer Engineering at University of Toronto
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
jupyter-notebook10
machine-learning10
python10
pytorch9
Programming languages (2)
Github contributions (5)
RussTedrake/underactuated
Mar 2021 - Apr 2021
The course text for MIT 6.832 (and 6.832x on edX)
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 6 commits, 13 PRs in 1 month
Contributions summary:Dongchan's commits primarily focus on modifying and extending exercises related to machine learning within the repository. They fixed import errors in a Hopfield network exercise, disabled a Bazel test, added and updated a gradient flow exercise with improvements, and added a new exercise on linear system identification. These contributions indicate a focus on hands-on learning and practical application of machine learning concepts within the context of an educational resource.
edxpythonmit
dclee131/dclee131.github.io
Jan 2019 - Jun 2022
Contributions:192 commits, 9 pushes, 2 issues in 3 years 5 months
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