Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

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.”

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.

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

A deep learning model improves the ability to identify genes potentially involved in disease.

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

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

KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

As the volume and complexity of data captured around the world continues to grow exponentially, new ways of exploring and visualizing this data are required. Today, society has moved beyond the traditional desktop computer with tools such as augmented and virtual reality (AR/VR) at the forefront of immersive data visualization and analysis.

 David Keyes, KAUST professor of applied mathematics and computational science; director of the University's Extreme Computing Research Center; and senior associate to the President of KAUST, will chair the International Supercomputing Conference (ISC) 2020. He will be the first program chair from a Middle Eastern institution, as announced by ISC 2020.