Haonan Yu
Member Of Technical Staff at Skild AI
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHaonan Yu is a robotics-focused researcher-engineer with 11 years of experience building learning-driven systems that bridge perception, language grounding, and control for real-world robots. Currently a Member of Technical Staff at Skild AI in San Jose, he applies embodied AI methods to bring multi-skill robots into everyday scenarios. Previously he led research and engineering efforts at Horizon Robotics—co-authoring the ALF RL framework and contributing to SLIM, a quadruped manipulator project—after stints at Meta and Baidu on vision-language grounding and simulation-based RL. He holds a Ph.D. in Electrical and Computer Engineering from Purdue and a B.S. in Computer Science from Peking University, combining strong academic foundations with production-minded implementation. Notably, his work spans both foundational RL algorithm design (efficiency and safety) and the engineering of research platforms that enable RL-in-real deployments.
11 years of coding experience
9 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science, Bachelor of Science (B.S.) Computer Science at Peking UniversityDoctor of Philosophy (Ph.D.) Electrical and Computer Engineering, Doctor of Philosophy (Ph.D.) Electrical and Computer Engineering at Purdue University
English, Chinese
Github Skills (29)
pytorch10
distributed-training10
parallel10
parallelization10
python10
mahjong10
machine-learning10
industrial10
reinforcement-learning10
deep-learning10
tensorflow10
speech10
neural-network10
paddlepaddle10
scalability10
Programming languages (4)
Github contributions (5)
yu239-zz/Paddle
Sep 2016 - Mar 2018
Contributions:1 PR, 42 pushes, 38 branches in 1 year 5 months
paralleldeep-learningmachine-learningdistributed-deep-learningdistributed
yu239/XWorld
Aug 2017 - Sep 2018
A C++/Python simulator package for Reinforcement Learning
Contributions:4 PRs, 106 pushes, 6 branches in 1 year 1 month
pythonreinforcement-learningsimulationsimulatorreinforcement
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