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. The specific topics I have been interested in include low-rank random matrix models, statistical network analysis, nonparametric Bayes, computer models, and uncertainty quantification. On the application side, I am also interested in designing new Bayesian methods for computational biology.
I received my Ph.D. from the Department of Applied Mathematics and Statistics at Johns Hopkins University under the supervision of Yanxun Xu.
Contact
Myles Brand Hall E210C
901 E. 10th Street
Bloomington IN 47408
fxie at iu dot edu
Education
- Ph.D. in Applied Mathematics and Statistics, Johns Hopkins University, 2020
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Bayesian Sparse Gaussian Mixture Model in High Dimensions
Dapeng Yao, Fangzheng Xie, Yanxun Xu (Technical report) [arXiv] -
Statistical inference of random graphs with a surrogate likelihood function
Dingbo Wu, Fangzheng Xie (Technical report) [arXiv] [R Package] -
An Eigenvector-Assisted Estimation Framework for Signal-Plus-Noise Matrix Models
Fangzheng Xie, Dingbo Wu
Biometrika, accepted for publication, 2023. [Link] -
Entrywise limit theorems for eigenvectors of signal-plus-noise matrix models with weak signals
Fangzheng Xie
Bernoulli, accepted for publication, 2023. [Link] [arXiv] -
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