Yandi Shen

Assistant Professor

Bio

I am an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University.

Education

In 2021, I obtained my Ph.D. in Statistics at the University of Washington, advised by Fang Han and Daniela Witten. I was then a Kruskal instructor in Statistics at University of Chicago from 2021 to 2023, and a postdoctoral researcher hosted by Zhou Fan at the Department of Statistics and Data Science at Yale University.

Research

I am broadly interested in nonparametric and semiparametric statistics, high dimensional inference, and applied probability.

Publications

•  Z. Fan, L. Guan, Y. Shen, and Y. Wu (2023) Gradient flows for empirical Bayes in high-dimensional linear models. [arxiv]
• X. Xu, Y. Shen, Y. Chi, and C. Ma (2023) The power of preconditioning in overparameterized low-rank matrix sensing. [arxiv]
• Y. Shen and Y. Wu (2022) Empirical Bayes estimation: When does g-modeling beat f-modeling in theory (and in practice)? [arxiv]
• Q. Han and Y. Shen (2022) Universality of regularized regression estimators in high dimensions. Ann. Statist. [journal] [arxiv]
• F. Han, Z. Miao, and Y. Shen (2021) Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance. IEEE Trans. Inf. Theory [journal] [arxiv]
• Q. Han and Y. Shen (2021) Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality. [arxiv]
• Q. Han, T. Jiang, and Y. Shen (2021) A general method for power analysis in testing high dimensional covariance matrices. To appear at Ann. Appl. Probab. [arxiv]
• C. Gao, Y. Shen, and A.Y. Zhang (2021) Uncertainty quantification in the Bradley-Terry-Luce model. To appear at Inf. Inference [journal] [arxiv]
• Y. Shen, Q. Han, and F. Han (2022) On a phase transition in general order spline regression. IEEE Trans. Inf. Theory [journal][arxiv]
• Q. Han, B. Sen, and Y. Shen (2022) High dimensional asymptotics of likelihood ratio tests in Gaussian sequence model under convex constraint. Ann. Statist. [journal] [arxiv]
• Y. Shen, C. Gao, D. Witten, and F. Han (2020) Optimal estimation of variance in nonparametric regression with random design. Ann. Statist. [journal][arxiv]
• Y. Shen, F. Han, and D. Witten (2020) Exponential inequalities for dependent V-statistics via random Fourier features. Electron. J. Probab. [journal][arxiv]
• Y. Shen, F. Han, and D. Witten (2019) Tail behavior of dependent V-statistics and its applications. [arxiv]

Areas of Research

• Causal Inference
• Nonparametric Methods
• Foundations of Inference
• Optimization & Algorithms
• Statistical Machine Learning
• High-Dimensional Statistics