Yandi Shen

Yandi Shen is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from the University of Washington (2021), advised by Fang Han and Daniela Witten. Prior to joining CMU, he was a Kruskal Instructor in Statistics at the University of Chicago (2021–2023) and a postdoctoral researcher at Yale University under Zhou Fan. His research focuses on nonparametric and semiparametric statistics, high-dimensional inference, applied probability, and optimization algorithms.

Education: Ph.D. in Statistics (University of Washington, 2021); Postdoctoral work at University of Chicago and Yale University.

Research Interests:

• Nonparametric and semiparametric methods
• High-dimensional statistical inference
• Applied probability and stochastic processes
• Empirical Bayes methods
• Optimization and algorithm design
• Statistical machine learning

Teaching Experience:

• University of Washington: Teaching Assistant for advanced inference courses (STAT 581-583), probability (STAT 394-395), and applied statistics (STAT 528).
• University of Chicago: Instructor for courses like STAT 22400 (Applied Regression Analysis) and STAT 33611 (Gaussian Processes).

Recent Research Trends: His publications emphasize theoretical advancements in high-dimensional statistics, including empirical Bayes estimation, gradient flows, and optimization in overparameterized models. Key areas include analyzing phase transitions in spline regression, universality in regularized estimators, and nonparametric mixture models under optimal transport distances.

Labs/Teams: While no specific lab is named, his collaborative work spans statistical theory and applications, often involving interdisciplinary teams in data science and machine learning.