Zhou Fan

· Associate Professor of Statistics & Data Science

Yale University · Department of Statistics and Data Science

Active 2001–2025

h-index14

Citations567

Papers10566 last 5y

Funding$583k1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Zhou Fan is an Associate Professor in the Department of Statistics and Data Science at Yale University. His research interests lie at the intersection of mathematical statistics, probability theory, and computational algorithms. He focuses on inferential problems that arise in scientific applications, particularly in statistical genetics and computational biology. His work involves developing theoretical and computational methods to address complex statistical challenges in these fields.

Research topics

Computer Science
Algorithm
Mathematics
Sociology
Statistics
Theoretical computer science
Cartography
Combinatorics
Mathematical optimization
Engineering

Selected publications

Improving fine-mapping by modeling infinitesimal effects
Nature Genetics · 2023-11-30 · 65 citations
articleOpen accessCorresponding
Publisher OA PDF DOI
Approximate Message Passing algorithms for rotationally invariant matrices
The Annals of Statistics · 2022 · 65 citations
1st authorCorresponding
Approximate Message Passing (AMP) algorithms have seen widespread use across a variety of applications. However, the precise forms for their Onsager corrections and state evolutions depend on properties of the underlying random matrix ensemble, limiting the extent to which AMP algorithms derived for white noise may be applicable to data matrices that arise in practice. In this work, we study more general AMP algorithms for random matrices W that satisfy orthogonal rotational invariance in law, w…
Publisher OA PDF DOI
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory
International Conference on Machine Learning · 2020 · 25 citations
1st authorCorresponding
Spectral Graph Matching and Regularized Quadratic Relaxations II
Foundations of Computational Mathematics · 2022-06-13 · 21 citations
article1st authorCorresponding
Publisher DOI
Empirical Bayes PCA in High Dimensions
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2022-01-28 · 17 citations
articleOpen accessSenior authorCorresponding
Abstract When the dimension of data is comparable to or larger than the number of data samples, principal components analysis (PCA) may exhibit problematic high-dimensional noise. In this work, we propose an empirical Bayes PCA method that reduces this noise by estimating a joint prior distribution for the principal components. EB-PCA is based on the classical Kiefer–Wolfowitz non-parametric maximum likelihood estimator for empirical Bayes estimation, distributional results derived from random m…
Publisher OA PDF DOI

Recent grants

CAREER: High-dimensional inference and applications to modern biology
NSF · $400k · 2022–2027
Non-Convex Landscapes and High-Dimensional Latent Variable Models
NSF · $183k · 2019–2022

Frequent coauthors

H B Wang
Shandong Provincial Hospital
48 shared
Lei Xu
Southwest University
28 shared
Haibo Wang
Shandong Institute of Metrology
24 shared
Jianfen Luo
Shandong University
20 shared
R J Wang
Shandong Provincial Hospital
20 shared
Xiuhua Chao
Shandong Provincial Hospital
18 shared
Mingming Wang
Second Hospital of Shandong University
14 shared
Yuechen Han
13 shared

Education

Ph.D., Statistics
Stanford University
2018

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