Shuangping Li

Shuangping Li is an Assistant Professor in the Department of Statistics and Data Science at Yale University. She was previously a Stein Fellow in the Department of Statistics at Stanford University (2022–2025). Her research lies at the intersection of probability theory, high-dimensional statistics, theoretical machine learning, and the theory of algorithms.

• Ph.D. in Applied and Computational Mathematics, Princeton University (2022)
• B.Sc. in Mathematics, University of Hong Kong

Her research interests include probability theory, high-dimensional statistics, theoretical machine learning, and theory of algorithms. She investigates foundational aspects of random constraint satisfaction problems, neural networks, spectral methods, and phase transitions in high-dimensional models. Her work often draws from statistical physics and combinatorics to explain algorithmic behavior.

The recent articles highlight a strong focus on binary perceptrons, clustering in network models, and algorithmic phase transitions. Keywords across publications include probability, theoretical computer science, machine learning, and statistical inference. Subfields reveal deep engagement with spin glass theory, discrepancy minimization, spectral embedding, and information-computation gaps.

Scientific awards include:

• Stein Fellow, Department of Statistics, Stanford University (2022–2025)

She has advised and taught at both Stanford and Yale, including courses such as Advanced Probability, Theory of Probability, and Stochastic Processes. She has organized seminars at Stanford and has delivered invited talks at institutions including Cornell, Duke, UC Berkeley, and Princeton. Her collaborative research involves prominent scholars such as Allan Sly, Emmanuel Abbe, and Tselil Schramm. There is no mention of external grants, but her postdoctoral fellowship suggests research funding support.

She is involved in academic service through organizing the Stanford Statistics and Probability Seminars. She maintains an active research presence with publications in top venues like STOC, FOCS, COLT, ICLR, and journals such as Annals of Probability and Annals of Statistics.