Insight into the spinning-induced scattering of sound could help create next-generation acoustic devices using new phonon modes.
New book Decision Trees with Hypotheses by Mohammad Azad, Igor Chikalov, Shahid Hussain, Mikhail Moshkov, and Beata Zielosko has been published online.
A deep-learning technique could offer accurate large-scale predictions in the field of geospatial statistics.
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.
A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.
KAUST Distinguished Professor of Applied Mathematics and Computational Science Dr. Peter Markowich has been named a member of the European Academy of Sciences and Arts (EASA). He will officially be inducted as a member at the organization's annual inauguration ceremony in early 2023.
CEMSE researchers Matheus Guerrero, Raphaël Huser and Hernando Ombao developed a new approach for analyzing seizures in epilepsy patients by applying extreme value theory to real EEG seizure data.
Athanasios (Thanos) Tzavaras has been elected Fellow of the European Academy of Sciences (EurASc). He was nominated for his contributions to the interface of nonlinear partial differential equations and applied mathematics in the physical sciences.
David Keyes's and Matteo Ravasi's cross-disciplinary project results in improved efficiency for seismic processing, with promising applications for the energy industry.
Prof. David Bolin has been selected for the American Statistical Association Section on Statistics and the Environment Early Investigator (ENVR) Award for his outstanding contributions to environmental statistics.
A new study addresses the difficulty in modeling atmospheric turbulence at sub-kilometer resolution, which is challenging due to atmospheric variability, meteorology and changeable terrain such as mountains and cities.
Machine learning techniques can provide accurate forecasting of the spread of viruses during pandemics. Under the supervision of Ying Sun and Fouzi Harrou, Yasminah Alali developed an approach that removes human bias and assumptions, predicting pandemic evolution more accurately.
KAUST’s Extreme Statistics Group has developed an improved statistical model for analyzing environmental data of extreme events, such as heavy rainfall or strong wind data.