Xiaotian Zheng

I am Research Fellow (postdoc) at the Centre for Environmental Informatics (CEI) in the National Institute for Applied Statistics Research Australia (NIASRA) at the University of Wollongong. My research at CEI, with Noel Cressie and Andrew Zammit Mangion, involves developing new statistical methods for understanding and predicting Antarctic biodiversity, as well as data fusion and statistical downscaling for regional climate variables with uncertainty quantification. The research is part of Securing Antarctica’s Environmental Future (SAEF), an Australian Research Council (ARC) Special Research Initiative that aims to understand, manage, and forecast the environmental changes taking place across the Antarctic region.

My research interests broadly lie in parametric and nonparametric methods for complex and dependent data, from a Bayesian perspective. I draw motivation from applications in ecology, environmental science, health, and economics. I am also interested in methods for high-dimensional data and scalable algorithms for large data sets. I received my Ph.D. in Statistical Science from the Department of Statistics at the University of California, Santa Cruz, where I worked on mixture modeling for non-Gaussian spatial and temporal processes, advised by Athanasios Kottas and Bruno Sansó.

Upcoming activities and recent news

Dec 2023: Talk at the 16th International Conference of the ERCIM WG on Computational and Methodological Statistics (hybrid conference) in Berlin.

Dec 2023: Talk at the Australian Statistical Conference (ASC) at the University of Wollongong.

Dec 2023: Talk at the Bayesian Nonparametrics Networking Workshop at Monash University.

Nov 2023: Seminar at the University of Sydney School of Economics.

Oct 2023: Seminar for the Global Climate Change Week (GCCW) at the University of Wollongong.

Aug 2023: Talk at the Joint Statistical Meetings (JSM) in Toronto.

July 2023: Talk at the TIES Regional Meeting (hybrid conference) in Peterborough.

July 2023: Talks at the 6th Spatial Statistics conference in Boulder.

May 2023: Our team within CEI in NIASRA won first place in one of the categories of the 2023 KAUST Competition on Spatial Statistics for Large Datasets.

May 2023: Talk at the 13th Workshop on Bayesian Inference for Stochastic Processes (BISP13) in Madrid.

Jan 2023: Student Paper Award from the Business and Economics Statistics Section of the ASA.

Dec 2022: Talk at the Time Series and Forecasting Symposium at the University of Sydney Business School.

Oct 2022: SAEF was launched!


  1.   Xiaotian Zheng, Athanasios Kottas, and Bruno Sansó (2023). “Mixture modeling for temporal point processes with memory”.
      (2023 ASA Business and Economics Statistics Section Student Paper Award)

Journal Papers

  3.   Xiaotian Zheng, Athanasios Kottas, and Bruno Sansó (2023). “Nearest-neighbor mixture models for non-Gaussian spatial processes”.
      Bayesian Analysis, to appear.
      (2022 ISBA Section on Environmental Sciences Student Paper Award)

  2.   Xiaotian Zheng, Athanasios Kottas, and Bruno Sansó (2023). “Bayesian geostatistical modeling for discrete-valued processes”.
      Environmetrics, 34(7), e2805. [arXiv] [code]

  1.   Xiaotian Zheng, Athanasios Kottas, and Bruno Sansó (2022). “On construction and estimation of stationary mixture transition distribution models”.
      Journal of Computational and Graphical Statistics, 31, 283-293. [arXiv] [code]
      (2021 ASA Section on Bayesian Statistical Science Student Paper Award)


  1.   R package and example code for “On Construction and Estimation of Stationary Mixture Transition Distribution Models.” [package] [example]


University of Wollongong (semester system)

  • Lecturer: Introduction to Statistics (STAT 101; Undergraduate; Spring 2023)

University of California, Santa Cruz (quarter system)

  • Graduate Student Instructor: Statistics Laboratory (STAT 7L; 3 quarters)
  • Teaching Assistant: Intermediate Bayesian Statistical Modeling (STAT 207; Graduate; 1 quarter); Classical and Bayesian Inference (STAT 132; Undergraduate; 1 quarter); Introduction to Probability Theory (STAT 131; Undergraduate; 3 quarters); Statistics (STAT 5 & STAT 7; Undergraduate; 8 quarters)