Xiaotian Zheng

I am Research Fellow at the Centre for Environmental Informatics (CEI) of 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 better understanding of the Antarctic environment, as well as data fusion and statistical downscaling for regional climate variables with uncertainty quantification. For example, we study Antarctic biodiversity in response to changes in climate processes, using the fused and/or downscaled climate variables. 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.

Recent News

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 Time Series and Forecasting Symposium at the University of Sydney Business School

Oct 2022: Official launch of the ARC initiative SAEF


  2.   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)

  1.   Xiaotian Zheng, Athanasios Kottas, and Bruno Sansó (2023). Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes.
      (2022 ISBA Section on Environmental Sciences Student Paper Award)

Journal Papers

  2.   Xiaotian Zheng, Athanasios Kottas, and Bruno Sansó (2023). Bayesian Geostatistical Modeling for Discrete-Valued Processes.
      Environmetrics, to appear.

  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 California, Santa Cruz

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