Peng Zhong

Peng Zhong

Postdoc Fellow in Statistics

  • I am currently a postdoc fellow at The University of New South Wales (UNSW) supervised by Prof. Scott Sission and Prof. Boris Beranger. 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, e.g., GAN and VAE (Variational Autoencoder).

  • If you want to know more about me, you can see my regular blog posts.

Publications

[1] Peng Zhong, Raphaël Huser, and Thomas Opitz, Modeling non-stationary temperature maxima based on extremal dependence changing with event magnitude, Annals of Applied Statistics, 16, 272-299, 2022 [PDF]

[2] Peng Zhong, Raphaël Huser, and Thomas Opitz, Exact simulation of max-infinitely divisible processes, Econometrics and Statistics, To appear, 2023+ [PDF]

[3] Zhongwei Zhang, Elias Krainski, Peng Zhong, Håvard Rue, and Raphaël Huser, Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes, To appear, 2023+ [arxiv]

Papers Under Review

[4] Raphaël Huser, Michael Stein, and Peng Zhong, Vecchia likelihood approximation for accurate and fast inference in intractable spatial extremes models, Submitted [arxiv]

[5] Yan Gong, Peng Zhong, Thomas Opitz, and Raphaël Huser, Partial tail-correlation coefficient applied to extremal-network learning, Submitted [arxiv]

[6] Peng Zhong, Manuela Brunner, Thomas Opitz, and Raphaël Huser, Spatial modeling and future projection of extreme precipitation extents, Submitted [arxiv]

Photograph