Assistant Professor at The Chinese University of Hong Kong, Shenzhen 香港中文大学(深圳)
New York, New York, United States
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Summary
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Senior
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Xiaopeng Li is an assistant professor at the School of AI, CUHK Shenzhen, with a PhD in Operations Research from Columbia and eight years of experience bridging continuous optimization theory and practical machine learning applications. His research combines semialgebraic geometry and ODE analysis to explain why first-order and momentum methods succeed in deep learning, and he has proven improved global convergence results while relaxing classical assumptions. Xiaopeng has strong industrial experience—solving large-scale nonconvex portfolio optimization at Citadel and developing scalable solvers and algorithms for stochastic MILP and transmission expansion at national labs. He has a track record of engineering impact, from integrating sparse direct solvers into large-scale LP interiors to implementing ADMM and progressive hedging in Python/Julia for HPC. Beyond theory and code, he runs an active optimization study group and enjoys translating deep mathematical insights into algorithms that yield measurable performance gains in real-world systems.
8 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS, Applied Mathematics, Bachelor of Science - BS, Applied Mathematics at The Chinese University of Hong Kong, Shenzhen 香港中文大学(深圳)
Doctor of Philosophy - PhD, Operations Research, Doctor of Philosophy - PhD, Operations Research at Columbia University in the City of New York
Contributions:37 commits, 24 pushes, 1 branch in 4 days
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