About Yan Gong Yan Gong Ph.D. Student, Statistics extreme-value theory Statistics of extremes copulas Bayesian Statistics risk assessment Yan Gong was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Raphaël Huser. Yan successfully defended her PhD thesis entitled " Flexible Multivariate, Spatial, and Causal Models for Extremes" on March 28th, 2023; see her PhD thesis here. Her PhD committee was composed of Professors Raphaël Huser (chair), Valérie Chavez-Demoulin (external examiner from HEC Lausanne at UNIL, Switzerland), David Bolin, and Mohammed-Slim Alouini. For her next career steps, Yan has accepted a short-term postdoctoral research position at Articles Related News January 2024 New paper accepted in Technometrics 1 min read · Wed, Jan 3 2024 Spotlight News New paper accepted: Gong, Y., Zhong, P., Opitz, T., and Huser, R. (2024+), Partial tail-correlation coefficient applied to extremal-network learning, Technometrics, to appear [ PDF preprint] September 2023 Top honors for KAUST extSTAT group at EVA 2023 2 min read · Wed, Sep 13 2023 Awards News KAUST extSTAT Research Group wins prestigious awards at EVA 2023 Conference in Milan. July 2023 Congratulations to Paolo and Yalla team for their success at the EVA2023 conference 1 min read · Thu, Jul 6 2023 News Spotlight We are thrilled to extend our warmest congratulations to Paolo Victor Redondo and Team Yalla for their outstanding achievements at the EVA 3023 conference! The event, which aims to bring together researchers in Extreme Value Theory (EVT), methods, and its applications, witnessed Paolo's remarkable win for the Best Poster Presentation and Team Yalla's triumphant victory in the EVA Data Competition. Paolo's Best Poster Presentation award demonstrates his commitment, hard work, and enthusiasm for EVT and biostatistics. His remarkable presenting abilities and groundbreaking work on brain January 2023 Two new papers in Extremes 1 min read · Thu, Jan 19 2023 Spotlight News Two new papers accepted to the Extremes Special Issue on the EVA Data Competition: 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, to appear [ PDF preprint]. Cisneros, D., Gong, Y., Yadav, R., Hazra, A., and Huser, R. (2022+), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes, to appear [ PDF preprint]. October 2022 Flexible modeling of multivariate spatial extremes 1 min read · Sun, Oct 30 2022 News New paper accepted: Gong, Y., and Huser, R. (2022+), Flexible modeling of multivariate spatial extremes, Spatial Statistics, to appear [ PDF preprint]. March 2022 Decrypting the interdependence of cryptocurrencies 1 min read · Tue, Mar 1 2022 News big data modeling statistics A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies. November 2021 KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon 3 min read · Mon, Nov 29 2021 News IoT agriculture artificial intelligence drones A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants. Asymmetric tail dependence modeling, with application to cryptocurrency market data 1 min read · Fri, Nov 5 2021 News New paper accepted: Gong, Y., and Huser, R. (2021+), Asymmetric tail dependence modeling, with application to cryptocurrency market data , Annals of Applied Statistics [ arXiv][ PDF preprint ].
New paper accepted in Technometrics 1 min read · Wed, Jan 3 2024 Spotlight News New paper accepted: Gong, Y., Zhong, P., Opitz, T., and Huser, R. (2024+), Partial tail-correlation coefficient applied to extremal-network learning, Technometrics, to appear [ PDF preprint]
Top honors for KAUST extSTAT group at EVA 2023 2 min read · Wed, Sep 13 2023 Awards News KAUST extSTAT Research Group wins prestigious awards at EVA 2023 Conference in Milan.
Congratulations to Paolo and Yalla team for their success at the EVA2023 conference 1 min read · Thu, Jul 6 2023 News Spotlight We are thrilled to extend our warmest congratulations to Paolo Victor Redondo and Team Yalla for their outstanding achievements at the EVA 3023 conference! The event, which aims to bring together researchers in Extreme Value Theory (EVT), methods, and its applications, witnessed Paolo's remarkable win for the Best Poster Presentation and Team Yalla's triumphant victory in the EVA Data Competition. Paolo's Best Poster Presentation award demonstrates his commitment, hard work, and enthusiasm for EVT and biostatistics. His remarkable presenting abilities and groundbreaking work on brain
Two new papers in Extremes 1 min read · Thu, Jan 19 2023 Spotlight News Two new papers accepted to the Extremes Special Issue on the EVA Data Competition: 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, to appear [ PDF preprint]. Cisneros, D., Gong, Y., Yadav, R., Hazra, A., and Huser, R. (2022+), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes, to appear [ PDF preprint].
Flexible modeling of multivariate spatial extremes 1 min read · Sun, Oct 30 2022 News New paper accepted: Gong, Y., and Huser, R. (2022+), Flexible modeling of multivariate spatial extremes, Spatial Statistics, to appear [ PDF preprint].
Decrypting the interdependence of cryptocurrencies 1 min read · Tue, Mar 1 2022 News big data modeling statistics A team of KAUST statisticians has analyzed the price movements of the top five cryptocurrencies to further understand their market behavior. Their model will help investors assess the risks of investing in cryptocurrencies.
KAUST-KFUPM collaboration wins top honors at STC AIoT Hackathon 3 min read · Mon, Nov 29 2021 News IoT agriculture artificial intelligence drones A team composed of KAUST and King Fahd University of Petroleum and Minerals (KFUPM) students was one of three winning teams of this year’s Saudi Telecom Company (STC) AIoT (AI & IoT) Hackathon. The KAUST entry, AgriDoctor, a proposed autonomous, sustainable, intelligent agriculture system, was a standout pick for the competition judges from a pool of 110 project submissions and 350 participants.
Asymmetric tail dependence modeling, with application to cryptocurrency market data 1 min read · Fri, Nov 5 2021 News New paper accepted: Gong, Y., and Huser, R. (2021+), Asymmetric tail dependence modeling, with application to cryptocurrency market data , Annals of Applied Statistics [ arXiv][ PDF preprint ].
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