Fangzheng Xie

There is no essential difference between a frequentist and a Bayesian.

About Me

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.

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Contact

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

Publications and Preprints
Bayes Theory and Methods for High-dimensional Data
  • 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]
Bayes Theory and Methods for Nonparametric Statistics:
  • 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]
Uncertainty Quantification:
  • 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]
Bayesian Methods for Computational Biology
Awards
  • 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
Academic Presentations
  • 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
Teaching
  • STAT-S 520 Introduction to Statistics (Fall 2020)