Yan Gong
Visiting Assistant Professor of Statistics and Data Science
Research Interests
Professor Gong’s research interests include developing and applying modern advanced statistical methodologies to address complex, real-world high-impact problems, including challenges in financial risk, extreme weather events, and public health. Her work spans statistics of extremes, spatial statistics, causal inference, Bayesian inference, and deep learning.
Email
Prior to joining MBZUAI, Professor Gong held postdoctoral positions at Harvard T. H. Chan School of Public Health and Imperial College London. She received her Ph.D. in Statistics from King Abdullah University of Science and Technology (KAUST) in 2023, under the supervision of Prof. Raphael Huser.
Professor Gong currently serves as a founding organizer of the Spatio-Temporal Statistics and Data Science Community (STSDS), an international network dedicated to advancing research and collaboration in spatio-temporal statistics and data science.
- Postdoctoral Research Fellow, Harvard T.H. Chan School of Public Health
- Postdoctoral Research Fellow, Imperial College London
- Ph.D. in Statistics, King Abdullah University of Science and Technology (KAUST)
- Master of Science in Statistics, King Abdullah University of Science and Technology (KAUST)
- Bachelor of Science in Mathematics and Applied Mathematics, Xi'an Jiaotong University.
- First prize winning team, Data Challenge at Extreme Value Analysis Conference (EVA) 2023, Milan, Italy
- Track winning team, JUNCTION 2018, Helsinki, Finland
- Siyuan Scholarship, Award for Academic Excellence, Xi'an Jiaotong University, Xi'an, China
- Partial tail-correlation coefficient applied to extremal-network learning. Y Gong, P Zhong, T Optiz, R Huser. Technometrics 66 (3), 331-346, 2024.
- A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes. D Cisneros, Y Gong, R Yadav, A Hazra, R Huser. Extremes 26 (2), 301, 2023.
- Flexible modeling of multivariate spatial extremes. Y Gong, R Huser. Spatial Statistics 52, 100713, 2002.
- Asymmetric tail dependence modeling, with application to cryptocurrency market data. Y Gong, R Huser.Annals of Applied Statistics 16 (3), 1822-1847, 2022.