Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Dominik L. Michels, Assistant Professor of Computer Science and Applied Mathematics, and Head of the Computational Sciences Research Group within KAUST's Visual Computing Center, was recently awarded one of the six Artificial Intelligence Grants of the State of North Rhine-Westphalia (NRW), Germany, for his contributions to the simulation of complex physical environments. The grant, amounting to 1.25 million euros, will fund Michels’ research on algorithmic methods to use synthetic data for training of neural networks in Machine Learning. “Synthetic data are data that were not obtained by direct measurement but were generated by specific algorithms,” Michels explains, “in neural networks, the use of synthetic data is needed whenever the amount of data available is less than required.”

Mohamed Eddaoudi, KAUST distinguished professor of chemical science and director of the University's Advanced Membrane & Porous Materials Center, recently received the Abdul Hameed Shoman Award for Arab Researchers for Renewable Energy, an award presented annually by the Abdul Hameed Shoman Foundation. Omar Knio, KAUST professor of applied mathematics and computational science, was also honored by the Abdul Hameed Shoman Foundation this year as a joint recipient of the Shoman Award for Mathematical Modeling with Dr. Shaher Mohammed Ahmad Momani.

A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.

More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.

Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Marc Genton, Chair and Distinguished Professor of the Statistics Program of KAUST and Head of the Spatio-Temporal Statistics and Data Science Research Group, received an International Statistical Institute (ISI) Service Award 2019 for his efforts and leadership as Editor-in-Chief of the journal Stat. Professor Genton received his award at the Awards Ceremony during the World Statistical Congress (WSC) 2019 held in Kuala Lumpur, Malaysia, 18-23 August 2019.  

Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

KAUST Assistant Professor Raphaël Huser recently received the American Statistical Association's (ASA) 2019 Section on Statistics and the Environment (ENVR) Early Investigator Award for his outstanding contributions to environmental statistics. He accepted the award at the 2019 Joint Statistical Meetings held in Denver, Colorado, U.S., from July 27 to August 1.

Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's  Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

Mapping variations in sunlight across the Arabian Peninsula reveals a bright future for solar energy in the region.

A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.

Computer simulation accurately captures the beguiling motion of a liquid magnetic material.