Fangzheng Xie

I am an assistant professor in the Department of Statistics at Indiana University. My major research focuses on developing Bayesian methods for various statistical problems with strong theoretical support. Specifically, I have been focusing on Bayes theory and methods for network data and high-dimensional problems, nonparametric Bayes, and Bayesian methods for computer experiments 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 Dr. Yanxun Xu.

• Optimal Bayesian estimation for random dot product graphs
  Biometrika Accepted for publication, December 2019
  Fangzheng Xie and Yanxun Xu [arXiv]
• Efficient Estimation for Random Dot Product Graphs via a One-step Procedure
  (Journal of the American Statistical Association, under revision)
  Fangzheng Xie and Yanxun Xu [arXiv]
• Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
  (Submitted)
  Fangzheng Xie, Yanxun Xu , Carey E. Priebe, and Joshua Cape [arXiv]

• Rates of Contraction with respect to L₂-distance for Bayesian Nonparametric Regression
  Fangzheng Xie, Wei Jin, and Yanxun Xu
  Electronic Journal of Statistics, 2019, Vol. 13, No. 2, 3485-3512.
  [Link]
• Adaptive Bayesian Nonparametric Regression using a Kernel Mixture of Polynomials with Application to the Partial Linear Model
  Fangzheng Xie and Yanxun Xu
  Bayesian Analysis, Accepted for Publication January 2019
  [Accepted Manuscript]
• Bayesian Repulsive Gaussian Mixture Model
  Fangzheng Xie and Yanxun Xu
  Journal of the American Statistical Association (Theory and Methods)
  Accepted for Publication October 2018
  (Winner of the 2017 O-Bayes Young Investigator Travel Award)
  [Accepted Manuscript]

• A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration
  (Submitted)
  Mengyang Gu, Fangzheng Xie, and Long Wang [arXiv]
• Bayesian Projected Calibration of Computer Models
  Journal of the American Statistical Association (Theory and Methods)
  Accepted for Publication 2020
  Fangzheng Xie and Yanxun Xu [arXiv] [R Package]

• BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts
  Fangzheng Xie, Mingyuan Zhou, and Yanxun Xu
  Annals of Applied Statistics, 2018, Vol. 12, No. 3, 1605-1627.
  [Link] [R Package] [Installation R Script]
• BAREB: A Bayesian repulsive biclustering model for periodontal data
  Statistics in Medicine, 2020; 39: 2139–2151.
  Yuliang Li, Yanxun Xu, Fangzheng Xie, and Dipankar Bandyopadhyay [Link]

• Acheson J. Duncan Fund for the Advancement of Research in Statistics Travel Award, 2017 - 2020
• O-Bayes 2017 Young Investigator Travel Award, December 2017
• Rufus P. Isaacs Graduate Fellowship, 2017 - 2020
• Excellent Undergraduate Dissertation Award, South China University of Technology, June 2014
• Merit-Based Scholarship, South China University of Technology, 2011-2013
• China Undergraduate Mathematics Competition, Second place (Top 10%), October 2012

• Bayesian Projected Calibration of Computer Models
  Joint Statistical Meeting 2019, Denver, CO, July 2019
  Johns Hopkins University, Baltimore, MD, February 2019
• Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
  Johns Hopkins University, Baltimore, MD, September 2018
• A Theoretical Framework for Bayesian Nonparametric Regression
  Joint Statistical Meeting 2018, Vancouver, BC, Canada, July 2018
  Johns Hopkins University, Baltimore, MD, February 2018
• Bayesian Repulsive Gaussian Mixture Model
  O-Bayes17, Austin, TX, December 2017
  Johns Hopkins University, Baltimore, MD, November 2017

• STAT-S 520 Introduction to Statistics (Fall 2020)