Bohai Zhang

Honorary Research Fellow
School of Statistics and Data Science, Nankai University, China and 
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia

Email bohaizhang {at}
Curriculum Vitae Curriculum Vitae

Research Interests

Statistical methodology for modelling large spatial/spatio-temporal datasets; uncertainty quantification of large computer experiments; Gaussian process models; composite likelihood method; Bayesian hierarchical modelling and Bayesian clustering/partitioning methods.


Past and current projects include: spatio-temporal modelling of Eastern United States ozone datasets, uncertainty quantification of a carbon capture unit, spatial modelling of yearly total precipitation anomalies in the United States, validation and bias correction of remote sensing data, and spatio-temporal dynamic modeling of Arctic sea-ice extent data.



Zhang, B., Cressie, N., and Wunch, D. (2018). Inference for errors-in-variables models in the presence of systematic errors with an application to a satellite remote sensing campaign. Technometrics, 60, forthcoming.

Zhang, B. and Cressie, N. (2018). Estimating spatial changes over time of Arctic sea ice using hidden 2x2 tables. Journal of Time Series Analysis, forthcoming.


Zhang, B., Cressie, N., and Wunch, D. (2017). Statistical properties of atmospheric greenhouse gas measurements looking down from space and looking up from the ground. Chemometrics and Intelligent Laboratory Systems, 162, 214-222.

Cressie, N., Burden, S., Shumack, C., Zammit-Mangion, A., and Zhang, B. (2017). Environmental Informatics. Wiley StatsRef : Statistics Reference Online, pp. 1-8 (doi:10.1002/9781118445112.stat07717.pub2).


Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., and Lin, G (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics, 300, 623-642.

Zhang, B., Sang, H., and Huang, J.Z. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25, 99-114.


Last reviewed: 17 March, 2020