Shuyao Li

Shuyao Li is a Researcher at Meta, focusing on recommendation systems and algorithmic robustness. They earned a Ph.D. in Computer Science from the University of Wisconsin-Madison under co-advisors Jelena Diakonikolas and Stephen Wright, and a B.S. in Mathematics from the University of Hong Kong (supervised by Xiaoming Yuan). They also collaborated with Ernest Ryu at UCLA in 2019.

• Ph.D.: University of Wisconsin-Madison, Computer Science
• B.S.: University of Hong Kong, Mathematics

Their research integrates optimization, computational learning theory, and robustness guarantees in algorithm design. Key contributions include adversarial data contamination mitigation, robust single-neuron learning, and nonconvex optimization frameworks for low-rank matrix sensing. They combine theoretical rigor with practical applications in machine learning systems.

Their publications highlight trends in robust machine learning, nonconvex optimization, and algorithmic fairness. Recent work addresses distributional robustness, adversarial label noise, and complexity guarantees for stochastic optimization methods. This aligns with cross-disciplinary efforts in computer science, applied mathematics, and data science.

Scientific Awards:

• NeurIPS 2023 Scholar Award
• IFDS RAship (Spring 2022)
• UW Madison CS Departmental Research Fellowship (Fall 2020–Spring 2021)
• Outstanding Winner at Mathematical Contest in Modeling (MCM 2017)