Marc Genton, chair, and distinguished professor of the KAUST Statistics Program, was recently selected as the 2020 Georges Matheron Lecturer of the International Association for Mathematical Geosciences (IAMG).
A better mathematical understanding of how big waves form could lead to better prediction of tsunami impacts.
Gaurav Agarwal, a fourth-year Ph.D. student in KAUST Associate Professor Ying Sun’s Environmental Statistics (ES) research group, recently won the best student paper award at the International Indian Statistical Association (IISA) 2019 Student Paper Competition for his paper titled “Bivariate Functional Quantile Envelopes with Application to Radiosonde Wind Data.”
-By A. Ortega, KAUST News
KAUST Associate Professor of Electrical Engineering Taous-Meriem Laleg-Kirati joined the University in 2011 after working as a researcher at INRIA in France.
Joining the faculty of King Abdullah University of Science and Technology (KAUST) six years ago gave statistics Professor Ying Sun a unique career opportunity: the chance to build and lead her own research group solving real-world problems.
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Science and Engineering division, was recently elected as a Fellow of the Optical Society (OSA) at the society's Board of Directors meeting in September.
Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.
Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.
Ajay Jasra is a professor of applied mathematics and computational sciences who recently joined the KAUST CEMSE Division in July 2019.
Andrea Fratalocchi, associate professor in the University's Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently elected as a Fellow Member of The Optical Society of America (OSA) at the Society’s Board of Directors meeting in September.
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.
David Bolin is an associate professor of mathematical statistics who joined the KAUST Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division earlier this month from the Department of Mathematical Sciences at the University of Gothenburg.
Before joining KAUST, Bolin received both his Ph.D. degree in mathematical statistics and M.S.c in engineering mathematics from Lund University, Sweden, in 2012 and 2007, respectively. Upon completing his Ph.D., he spent one year at Umeå University, Sweden, working as a postdoctoral fellow before moving to the Chalmers University of Technology. In 2016, Bolin became an associate professor of mathematics at the University of Gothenburg, where a year later he received the title of Docent in mathematical statistics.
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.
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.”