KAUST Ph.D. alumna Sabrina Vettori and Ph.D. student Yuxiao Li received a Distinguished Student Paper Award at the 2018 Eastern North American Region (ENAR) Spring Meeting of the International Biometric Society. Li's paper focused on efficient estimation for non-stationary spatial covariance functions, while Vettori's paper addressed Bayesian hierarchical modelling of air pollution extremes. Both students were recognized for their contributions to statistical environmental studies and air pollution modeling. Why it matters: This award highlights KAUST's commitment to fostering high-quality research and recognizes the achievements of its students in addressing critical environmental challenges.
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.
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.
Gaurav Agarwal, a statistics Ph.D. student in the Environmental Statistics Group at KAUST, is researching statistical methods with environmental applications, such as understanding salt tolerance in plants. He is developing a user-friendly web application to make these methods accessible to those with limited statistical backgrounds. Agarwal also focuses on data visualization and outlier detection techniques for quality control of radiosonde wind data. Why it matters: This research contributes to environmental science by providing accessible statistical tools and methods for analyzing complex environmental data, potentially aiding in addressing challenges like plant resilience and climate monitoring.
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 Ph.D. student Sabrina Vettori won the 2017 Student Paper Competition sponsored by the Section on Statistics and the Environment of the American Statistical Association. Her winning paper was titled "Bayesian clustering and dimension reduction in multivariate air pollution extremes", co-authored by Huser and Genton. The competition focused on environmental statistics, with winners presenting at the Joint Statistical Meetings. Why it matters: This award recognizes KAUST's contribution to environmental statistics and highlights the university's ability to attract and nurture talent in this critical area.
KAUST Ph.D. student Ghulam Qadir received a best poster award at the GRASPA 2019 conference in Italy. The winning poster, titled "Estimation of Spatial Deformation for Non-stationary Processes via Variogram Alignment," was based on Qadir's Ph.D. research project. The research focuses on developing covariance models for multivariate nonstationary random fields with applications to environmental data. Why it matters: This award recognizes KAUST's contribution to environmental statistics and highlights the university's commitment to advancing research in this area.
KAUST postdoctoral fellow Carolina Euán received the Sylvia Esterby Presentation Award from the International Environmentrics Society (TIES) for her talk on a spatio-temporal model applied to drought data in Mexico. The research, conducted with KAUST Associate Professor Ying Sun, focuses on modeling dependence between processes observed in two categories, such as dry or rainy days. Euán joined KAUST in 2016 after completing her Ph.D. in statistics from the Research Center in Mathematics (CIMAT), Guanajuato, Mexico. Why it matters: This award recognizes the quality of environmental statistics research being conducted at KAUST and its applicability to understanding complex environmental phenomena in the region and beyond.