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.
This paper introduces a novel fuzzy clustering method for circular time series based on a new dependence measure that considers circular arcs. The algorithm groups series generated from similar stochastic processes and demonstrates computational efficiency. The method is applied to time series of wind direction in Saudi Arabia, showcasing its practical potential.
This talk discusses the asymptotic study of large asymmetric spiked tensor models. It explores connections between these models and equivalent random matrices constructed through contractions of the original tensor. Mohamed El Amine Seddik, currently a senior researcher at TII in Abu Dhabi, presented the work. Why it matters: The research provides theoretical foundations relevant to machine learning algorithms that leverage low-rank tensor structures, potentially impacting AI research and applications 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 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.
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.
Emilio Porcu from Khalifa University presented on temporally evolving generalized networks, where graphs evolve over time with changing topologies. The presentation addressed challenges in building semi-metrics and isometric embeddings for these networks. The research uses kernel specification and network-based metrics and is illustrated using a traffic accident dataset. Why it matters: This work advances the application of kernel methods to dynamic graph structures, relevant for modeling evolving relationships in various domains.
KAUST Assistant Professor Paula Moraga has authored a new textbook, "Spatial Statistics for Data Science: Theory and Practice with R," based on her lectures. The book is available for free on her website and in hard copy through the publisher. Dr. Moraga's research focuses on developing statistical methods and computational tools for geospatial data analysis and health surveillance, with applications in reducing disease burden and identifying high-risk populations. Why it matters: The publication strengthens KAUST's research profile in spatial data science and offers valuable open-source resources for addressing critical challenges in public health and resource management within Saudi Arabia and the broader region.