Professor Shinkyu Park has been awarded the 2022 O. Hugo Schuck Best Paper Award (Theory) from the American Automatic Control Council (AACC) for his paper investigating a new mathematical framework to study decision making in multi-agent systems.
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.
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.
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.
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.
Hossein Fariborzi's team has developed a novel miniature microelectromechanical device that avoids the need to use sensors and has significant potential for use in industrial or medical applications.
KAUST scientists have developed an optical-based wireless network system that has significant potential for faster underwater optical communications.
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.
Low Earth orbit (LEO) satellite plays an indispensable role in the equal access network because of its low latency, large capacity, and seamless global coverage. For such an unprecedented extensive irregular system, stochastic geometry (SG) is a suitable research method.
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.