KAUST Professor Marc Genton has been selected as the 2020 Georges Matheron Lecturer of the International Association for Mathematical Geosciences. Genton will present a lecture at the 36th International Geological Congress in Delhi, India, focusing on geostatistics, climate model outputs, and the ExaGeoStat software developed at KAUST. His lecture will cover Matheron's theory of regionalized variables and showcase ExaGeoStat, a high-performance software for geostatistics with exascale computing capability developed at KAUST. Why it matters: This recognition highlights KAUST's contributions to advanced statistical methods and high-performance computing in geosciences, enhancing its international reputation in these fields.
This paper introduces neural Bayes estimators for censored peaks-over-threshold models, enhancing computational efficiency in spatial extremal dependence modeling. The method uses data augmentation to encode censoring information in the neural network input, challenging traditional likelihood-based approaches. The estimators were applied to assess extreme particulate matter concentrations over Saudi Arabia, demonstrating efficacy in high-dimensional models. Why it matters: The research offers a computationally efficient alternative for environmental modeling and risk assessment in the region.
KAUST researchers from statistics and earth science collaborated to improve earthquake source modeling. They developed a statistical ranking tool to classify 2D fields, applicable to geoscience models like temperature or precipitation. The tool helps compare different 2D fields describing the earthquake source process and quantify inter-event variability. Why it matters: This cross-disciplinary approach enhances the reliability of earthquake rupture models, contributing to better hazard assessment and risk management in seismically active regions.
KAUST Professor Marc Genton has received the Royal Statistical Society’s (RSS) 2023 Barnett Award for his contributions to environmental statistics. Genton's work includes the development of ExaGeoStat, a high-performance software for geostatistics, and the use of spectral methods to emulate climate model outputs. His research includes a five-year study on wind energy potential in Saudi Arabia, informing the Kingdom’s national wind energy strategy. Why it matters: This award recognizes impactful environmental statistics research at KAUST with implications for Saudi Arabia's renewable energy sector and beyond.
KAUST Assistant Professor Raphaël Huser received the American Statistical Association's 2019 Section on Statistics and the Environment Early Investigator Award for his contributions to environmental statistics. Huser's research focuses on developing models for extreme events observed in space and time. He leads the KAUST extSTAT research group, which develops statistical models to understand the stochastic behavior of rare events. Why it matters: Recognition of KAUST faculty highlights the university's growing prominence in statistical research and its application to environmental challenges in the region.
Researchers propose a spatio-temporal model for high-resolution wind forecasting in Saudi Arabia using Echo State Networks and stochastic partial differential equations. The model reduces spatial information via energy distance, captures dynamics with a sparse recurrent neural network, and reconstructs data using a non-stationary stochastic partial differential equation approach. The model achieves more accurate forecasts of wind speed and energy, potentially saving up to one million dollars annually compared to existing models.
KAUST's HALO group launched a CubeSat in 2023 for high-precision Earth observation in the Gulf region, combining GNSS Reflectometry and hyperspectral sensing. The satellite monitors vegetation, soil, agriculture, and ecosystem health, providing detailed estimates of irrigation water use and vegetation health. The Extreme Statistics (XSTAT) research group at KAUST focuses on the mathematical modeling and prediction of extreme weather and climate events. Why it matters: These KAUST initiatives enhance climate resilience in the region through advanced monitoring, statistical modeling, and predictive capabilities.
KAUST Ph.D. student Yuxiao Li received a Student Paper Award from the American Statistical Association (ASA) for his paper on efficient estimation of non-stationary spatial covariance functions. The award-winning paper is Li's first research paper at KAUST, completed as a member of the Environmental Statistics Group led by Professor Ying Sun. His research focuses on short-term space-time precipitation modeling, addressing the challenges of modeling rainfall zeros and amounts along with complex spatio-temporal dependencies. Why it matters: This award recognizes KAUST's contributions to advanced statistical methods for environmental modeling, highlighting the university's strength in addressing complex environmental challenges.