A magnetic skin that is safe and comfortable to wear could open the door to a wide range of wireless, remotely controlled applications.
A deep learning tool could help in structure-based drug discovery.
Asrar Damdam is setting up her own biotech company in Silicon Valley while pursuing a Ph.D. at KAUST
Stretchy, see-through, silver nanowire sheet combines optical transparency with excellent electrical conductivity.
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.
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.
An inexpensive passive cooling technology could be used to cool buildings in cities, reducing energy consumption.
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.
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.
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.
Professors Maria Antónia da Conceição Abrantes Amaral Turkman and
Kamil Feridum Turkman, from the University of Lisbon used the
INLA methodology and specifically SPDE's (using R-INLA), to do the official
risk assessment for forest fire prediction in Portugal for 2018.
The spatial variation in different air pollution components helps identify possible targets for pollution control.
Macroalgae is shown to be a major global contributor to carbon sequestration.
Overlaying two film layers patterned with a nanoscale array can manipulate the propagation of light to create a powerful ultrathin lens.
Ruthenium oxide is used to integrate energy-storing microsupercapacitors and thin-film electronics at the transistor level.
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.
Exploring the links between natural climate cycles and the sea-surface temperature of the Red Sea reveals a cooling trend during the next few decades.
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.
A universal query engine for big data that works across computing platforms could accelerate analytics research.
Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.
Protein found in gut-dwelling pathogens changes shape under human body temperatures, leading to toxins responsible for diarrheal disease.
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.
A new kind of congenital disorder caused by a group of mutations in a gene previously linked only to a rare progressive brain disease has been discovered by an international team co-led by Saudi investigators.
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.
Mangroves, seagrass meadows, seaweeds and saltmarshes, the forests of the sea, perform a vital and often overlooked role in storing carbon and mitigating the effects of climate change.
Marine plastic pollution accumulates in mangrove forests and is a danger to sea life.
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.
Low-cost, stretchy sensors can be assembled inside the lid of a drug container to help monitor patient safety.
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.
Highlighting sustainability and the work of female researchers was a strong focus of the international KAUST-US NSF electronics conference.
Floating paper cubes containing low-cost, inkjet printed electronic sensors can wirelessly monitor floods in real time.
Metallic nanostructures absorb light better than any other known structures.
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.
An interdisciplinary initiative is helping KAUST be at the forefront of a digital revolution, where sensors can find a use just about anywhere.
A pioneering tagging system that monitors the movement and local environment of sea animals reaches deeper depths and higher sensitivities.
Fluorinated metal-organic frameworks make excellent materials for selective sensing and removal of toxic gases.
A tiny, portable radar device could allow visually impaired people, or unmanned moving devices, to detect objects in real time.
The vFabLab™ platform, short for Virtual Fabrication Lab, will be presented on April 24th during a webinar organized and hosted by the IEEE Electron Devices Society.
The latest statistical methods from research on complex high-dimensional environmental data also yield powerful tools for interpreting brain activity.
Three-dimensional (3D) computed tomography is a widely used technology that visualizes an object's external and internal structure by assembling a series of two-dimensional images taken sequentially across or around it.
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.
A simple fractional-order capacitor has been developed by a team from KAUST. Made from a single component, this device expands the range of frequencies that can be achieved by these devices, making them better at energy storage. Traditional analogue components are resistors, capacitors, and inductors.
Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.
Ambient energy emitted by cellular phones and modems can be captured and converted into electricity using unusually shaped technology.