• I am currently a postdoc fellow at The University of New South Wales (UNSW) supervised by Prof. Scott Sisson and Dr. Boris Beranger since February 2023. Before coming to UNSW, I earned my Ph.D. degree in Statistics at King Abdullah University of Science and Technology (KAUST) under Prof. Raphaël Huser’s supervision in June 2022. My research mainly focuses on modeling spatial extremes, high-dimensional inference, and Bayesian inference. In addition, I am also interested in deep learning frameworks and their application in the field of statistics.

Publications:

[1] Zhong P., Huser R., and Opitz T. (2022), Modeling non-stationary temperature maxima based on extremal dependence changing with event magnitude, Annals of Applied Statistics, 16 (1), 272-299.

[2] Zhong P., Huser R., and Opitz T. (2022), Exact simulation of max-infinitely divisible processes, Econometrics and Statistics, 30, 96-109.

[3] Zhang Z., Krainski E., Zhong P., Rue H., and Huser R. (2023), Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes, 26 (2), 339-351.

[4] Huser R., Stein M., Zhong P. (2024), Vecchia Likelihood Approximation for Accurate and Fast Inference with Intractable Spatial Max-Stable Models (as Part of my Ph.D. thesis), Journal of Computational and Graphical Statistics, 1-22.

[5] Gong Y., Zhong P., Huser R., and Opitz T. (2024), Partial tail-correlation coefficient applied to extremal-network learning, Technometrics, 1-16.

[6] Zhong P., Brunner M., Huser R., and Opitz T. (2024), Spatial modeling and future projection of extreme precipitation extents, Journal of the American Statistical Association, To appear.

Preprints:

[1] Zhong P., Beranger B., and Sisson S. (2024+) Flexible max-stable processes for fast and efficient inference, Submitted.