Nathan Zhao
Staff ML Scientist at XPENG
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolNathan Zhao is a staff ML scientist with 11 years of experience combining applied physics, numerical methods, and machine learning to solve real-world autonomy and robotics problems. With a PhD in applied physics from Stanford, he has built ML-driven motion prediction and collision risk systems for self-driving vehicles and now leads generative AI efforts for humanoid robotics at XPENG. His expertise spans linear algebra/PDE solvers, electrodynamics simulations, and scalable neural-network pipelines for AR/VR and perception stacks, reflecting a rare blend of theoretical and production-focused skills. Nathan has a strong track record of industry research at Cruise and Facebook Reality Labs and early-stage quantitative work at Akuna, plus hands-on experimental physics experience from Columbia, MIT, and Caltech. He is an active open-source practitioner—his GitHub collects numerical and ML projects that bridge simulation, optimization, and applied engineering.
11 years of coding experience
6 years of employment as a software developer
Bachelor of Arts (B.A.), Physics/Mathematics, Bachelor of Arts (B.A.), Physics/Mathematics at Columbia UniversityDoctor of Philosophy (PhD), Applied physics, Doctor of Philosophy (PhD), Applied physics at Stanford University
English
Github Skills (40)
fenics10
algorithms10
scipy10
spin10
matrix10
python10
wave10
photonics10
numba10
maxwell10
scientific-computing10
chemical-engineering9
julia9
finite-difference9
3d8
Programming languages (4)
Github contributions (5)
zhaonat/orbital_dynamics
Mar 2018 - Apr 2018
Contributions:28 commits, 25 pushes, 1 branch in 27 days
zhaonat/eigenwell
May 2021 - Oct 2021
finite difference based eigensolving for Maxwell's equations and photonic structures
Contributions:79 commits, 30 PRs, 63 pushes in 6 months
finite-differencemaxwell-equations-solvereigenvalueproblems
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