I am an assistant professor in the Department of Statistics at Indiana University. My research interests lie in developing theoretical foundations and computational tools for learning large and complex data, including spectral-based methods for low-rank random matrix models, statistical network analysis, high-dimensional statistics, and Bayesian nonparametrics.
I received my Ph.D. from the Department of Applied Mathematics and Statistics at Johns Hopkins University under the supervision of Yanxun Xu.
Email
fxie at iu dot edu
Education
- Ph.D. in Applied Mathematics and Statistics, Johns Hopkins University, 2020
- M.A. in Applied Mathematics and Statistics, Johns Hopkins University, 2016
- B.Sc. in Mathematics and Applied Mathematics, South China University of Technology, 2014
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Bias-Corrected Joint Spectral Embedding for Multilayer Networks with Invariant Subspace: Entrywise Eigenvector Perturbation and Inference
Fangzheng Xie (Technical report) [arXiv] -
Spectral Norm Posterior Contraction in Bayesian Sparse Spiked Covariance Matrix Model
Fangzheng Xie (Revision submitted) -
Higher-Order Entrywise Eigenvectors Analysis of Low-Rank Random Matrices: Bias Correction, Edgeworth Expansion, and Bootstrap
Fangzheng Xie, Yichi Zhang (Technical report) [arXiv] -
Bayesian Sparse Gaussian Mixture Model in High Dimensions
Dapeng Yao, Fangzheng Xie, Yanxun Xu (Revision submitted) [arXiv] -
Statistical inference of random graphs with a surrogate likelihood function
Dingbo Wu, Fangzheng Xie (Technical report) [arXiv] [R Package]
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An Eigenvector-Assisted Estimation Framework for Signal-Plus-Noise Matrix Models
Fangzheng Xie, Dingbo Wu
Biometrika, 2024; 111 (2), 661-676 [Link] -
Entrywise limit theorems for eigenvectors of signal-plus-noise matrix models with weak signals
Fangzheng Xie
Bernoulli, 2024; 30 (1), 388-418 [Link] -
Euclidean Representation of Low-Rank Matrices and Its Geometric Properties
Fangzheng Xie
SIAM Journal on Matrix Analysis and Applications, 2023; 44 (2), 822-866. [Link] -
Efficient Estimation for Random Dot Product Graphs via a One-step Procedure
Fangzheng Xie, Yanxun Xu
Journal of the American Statistical Association, 2023; 118 (541), 651-664. [Link] -
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration
Mengyang Gu, Fangzheng Xie, Long Wang
SIAM/ASA Journal of Uncertainty Quantification, 2022; 10 (4): 1435-1460. [Link] -
Simultaneous Learning the Dimension and Parameter of a Statistical Model with Big Data
Long Wang, Fangzheng Xie, Yanxun Xu
Statistics in Biosciences, accepted for publication, 2022. [Link] -
Bayesian Sparse Spiked Covariance Model with a Continuous Matrix Shrinkage Prior
Fangzheng Xie, Yanxun Xu, Carey Priebe, and Joshua Cape
Bayesian Analysis, 2022; 17 (4): 1193-1217. [Link] -
Bayesian Projected Calibration of Computer Models
Fangzheng Xie, Yanxun Xu
Journal of the American Statistical Association, 2022; 116 (536): 1965-1982. [Link] [R Package] -
BAREB: A Bayesian repulsive biclustering model for periodontal data
Yuliang Li, Dipankar Bandyopadhyay, Fangzheng Xie, and Yanxun Xu
Statistics in Medicine, 2020; 39 (16): 2139-2151. [Link] -
Optimal Bayesian Estimation for Random Dot Product Graphs
Fangzheng Xie, Yanxun Xu
Biometrika, 2020, 107 (4): 875-889. [Link] -
Bayesian Repulsive Gaussian Mixture Model
Fangzheng Xie, Yanxun Xu
Journal of the American Statistical Association, 2020; 115 (529): 187-203. [Link] -
Rates of Contraction with respect to L2-distance for Bayesian Nonparametric Regression
Fangzheng Xie, Wei Jin, Yanxun Xu
Electronic Journal of Statistics, 2019; 13 (2): 3485-3512. [Link] -
BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts
Fangzheng Xie, Mingyuan Zhou, Yanxun Xu
Annals of Applied Statistics, 2018; 12 (3): 1605-1627. [Link] [R Package] [Installation R Script] -
Adaptive Bayesian Nonparametric Regression using Kernel Mixture of Polynomials with Application to Partial Linear Model
Fangzheng Xie, Yanxun Xu
Bayesian Analysis, 2020; 15 (1): 159-186. [Link]
- STAT-S520 Introduction to Statistics, Fall 2020, Spring 2021, Fall 2023
- STAT-S771/772 Advanced Data Analysis, Fall 2023, Spring 2024
- STAT-S785 Seminar on Statistical Theory, Fall 2023, Spring 2024
- STAT-S721 Advanced Statistical Theory I, Fall 2021
- STAT-S722 Advanced Statistical Theory II, Spring 2022
- STAT-S350 Introduction to Statistical Inference, Fall 2022, Spring 2023