Jianqing Fan

About

Jianqing Fan is a statistician, financial econometrician, data scientist, and AI researcher. He holds the position of Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at Princeton University. He has served as the chair of the department from 2012 to 2015. His research focuses on statistics, finance, machine learning, and computational biology. Fan has received numerous awards including the 2000 COPSS Presidents' Award, the Morningside Gold Medal for Applied Mathematics in 2007, and the Guy Medal in Silver in 2014. He was elected as an Academician from Academia Sinica in 2012, a member of the Royal Flemish Academy of Belgium in 2023, and a member of the National Academy of Science in 2026. He is associated with multiple departments and centers at Princeton, including the Department of Economics, Department of Computer Science, Department of Electrical Engineering, and various research centers related to statistics, finance, and energy.

Research topics

• Computer Science
• Artificial Intelligence
• Statistics
• Machine Learning
• Mathematics
• Combinatorics
• Applied mathematics
• Discrete mathematics
• Algorithm
• Data science

Selected publications

• Statistical Foundations of Data Science

  Chapman and Hall/CRC eBooks · 2020 · 198 citations

  1st authorCorresponding

  Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as…

  DOI

• Entrywise eigenvector analysis of random matrices with low expected rank

  The Annals of Statistics · 2020 · 174 citations

  -spiked Wigner model) and noisy matrix completion.

  DOI

• A Selective Overview of Deep Learning

  Statistical Science · 2021 · 130 citations

  1st authorCorresponding

  Deep learning has achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a long history, recent advances have greatly improved their performance in computer vision, natural language processing, etc. From the statistical and scientific perspective, it is natural to ask: What is deep learning? What are the new characteristics of deep le…

  DOI

• Statistical Inference for High-Dimensional Matrix-Variate Factor Models

  Journal of the American Statistical Association · 2021 · 73 citations

  Senior authorCorresponding

  This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p × q) is comparable to or greater than the number of observations (T). We propose an estimation method called α-PCA that preserves the matrix structure and aggregates mean and contemporary covariance through a hyper-parameter α. We develop an inferential theory, establishing consistency, the rate of convergence, and the lim…

  DOI

• Inferences on mixing probabilities and ranking in mixed-membership models

  Journal of the American Statistical Association · 2026-05-18 · 3 citations

  preprintOpen access

  Network data is prevalent in numerous big data applications, including economics and health networks, where understanding the latent structure of the network is of prime importance. In this paper, we model the network using the Degree-Corrected Mixed Membership (DCMM) model. In the DCMM model, for each node <i>i</i>, there exists a membership vector πi=(πi(1),πi(2),…,πi(K)), where πi(k) denotes the weight that node <i>i</i> puts in community <i>k</i>. We derive a novel finite-sample expansion fo…

  PublisherOA PDFDOI

Recent grants

• Robust and Distributed Statistical Learning from Big Data

  NSF · $600k · 2017–2023

• DMS/NIGMS 2: Collaborative Research: Developing Statistical Learning Methods for Revealing the Molecular Signatures of Microvascular Changes in Neural Injury

  NSF · $450k · 2021–2026

• Statistical Methods for Ultrahigh-dimensional Biomedical Data

  NIH · $293k · 2006–2022

Frequent coauthors

• Yi Ren

  Guangxi Medical University

  131 shared

• Kai Cao

  University Radiology

  114 shared

• Wise Young

  Rutgers, The State University of New Jersey

  114 shared

• Lin Leng

  Yale University

  110 shared

• Richard Bucala

  Yale University

  110 shared

• Iman Tadmori

  110 shared

• Andreas Meinhardt

  Hudson Institute of Medical Research

  110 shared

• Changshun Shao

  Changchun University of Science and Technology

  110 shared

Education

• Ph. D., Department of Statistics

  University of California, Berkeley

  1989

• Masters, Department of Statistics

  Institute of Applied Mathematics, Chinese Academy of Science

  1985

• Bachelor, Department of Mathematics

  Fudan University

  1982

Awards & honors

• 2000 COPSS Presidents' Award
• Morningside Gold Medal for Applied Mathematics (2007)
• Guggenheim Fellow (2009)
• Pao-Lu Hsu Prize (2013)
• Guy Medal in Silver (2014)

Similar researchers at Princeton University

• Paul Seymourh-75
• Pramod Viswanathh-54
• Jason Leeh-53