uncertainty quantification

Abderrahmene Ben Romdhane

M.S. Student, Stochastic Numerics Research Group

numerical analysis Computational finance uncertainty quantification

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis uncertainty quantification optimal experimental design

Uncertainty-Aware Learning: From Bayesian Neural Networks to Agentic Decision Making

Theodore Papamarkou, Founder, PolyShape; Visiting Professor, School of Applied Mathematical and Physical Sciences (SEMFE), National Technical University of Athens (NTUA)

Feb 1, 12:00 - 13:00

B4/5 L0 A0215

uncertainty quantification neural network Bayesian modeling AI

This talk points out that uncertainty quantification is important for reliable AI, and that modern machine learning should be viewed through the lens of probabilistic decision making.

Amal Alghamdi

Founder, Impact Alpha, Saudi Arabia

HPC Python data analysis finite element method uncertainty quantification

ACM Gordon Bell Prize for Climate Modelling

KAUST wins the “Nobel” of high-performance computing for climate modeling

1 min read · Fri, Nov 22 2024

HPC climate projections extreme computing computational simulations uncertainty quantification

KAUST has been awarded the “Nobel" prize of high-performance computing—the ACM Gordon Bell Prize for Climate Modelling—in partnership with the NSF National Center for Atmospheric Research, U.S. and other partner institutions.

Uncertainty Quantification of Tsunami Models

Serge Guillas, Professor of Statistics, University College London (UCL)

Sep 8, 15:00 - 16:00

B1

uncertainty quantification Environmental Statistics

In this talk, we first show various strategies for the efficient emulation of simulators having uncertain inputs, with applications to tsunami wave modelling. A fast surrogate of the simulator's time series of outputs is provided by the outer product emulator.

International Agenda for KAUST's SRI-UQ

1 min read · Thu, Jul 24 2014

uncertainty quantification stochastic differential equations numerical methods

SIAM News published a piece on Professor Raul Tempone's Center for Uncertainty Quantification.

Cross-disciplinary collaboration results in groundbreaking earthquake research

1 min read · Wed, Mar 4 2015

uncertainty quantification earthquake source modelling

For KAUST research assistant Dr. Ling Zhang and Ph.D. student Hoby Razafindrakoto, a class in statistics the two took together on campus was a serendipitous event. "Hoby and Ling came to my spatial statistics class in the spring of 2013," explained Marc Genton, Professor of Statistics in the Computer, Electrical and Mathematical Science and Engineering Division. "They had to do a project for the class, and their project morphed into a collaboration that produced two papers and an online tool for uncertainty quantification in earthquake source modeling." The group effort included P. Martin Mai

Study finds Red Sea may be cooling rather than warming

1 min read · Thu, May 30 2019

Bayesian Estimation Geophysical Fluid Modeling uncertainty quantification

A recent study by a team including KAUST Earth scientists and oceanographers revealed that surface temperatures in the Red Sea may be cooling rather than rising. Analysis of long-term data sets shows that the current high warming rates of the Red Sea appear to be a combined effect of global warming and natural long-duration changes in sea surface temperature. "Our study has revealed a sequence of alternating positive and negative trends in Red Sea surface temperatures. Over the next decades, the trends indicate a cooling phase that may counter the effects of global warming," stated Ibrahim

Red Sea temperatures to cool in coming decades

1 min read · Sun, Jul 7 2019

geophysical fluid systems Bayesian Estimation uncertainty quantification

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 professors receive Abdul Hameed Shoman Award for Arab Researchers

1 min read · Sun, Oct 6 2019

Awards Spotlight News

uncertainty quantification bayesian inference computational fluids mechanics

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.

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Pratik Nag

Ph.D. Student, Statistics

spatio-temporal modeling machine learning artificial intelligence uncertainty quantification computational statistics

Pratik Nag is a Ph.D. candidate at the KAUST Environmental Statistics research group under the supervision of Professor Ying Sun. Before joining KAUST, Pratik obtained a master's degree in statistical quality control and operation research from the Indian Statistical Institute, India. Research Interests Pratik's research interests focus on machine learning, artificial intelligence and statistical techniques to solve problems related to spatio-temporal processes. Education Profile Master's degree in Statistical Quality Control and operation research, Indian Statistical Institute, India

Omar Knio

Dean, Computer, Electrical and Mathematical Sciences and Engineering

uncertainty quantification bayesian inference computational fluids mechanics combustion High Performance Computing

Professor Knio focuses on developing state-of-the-art methods and algorithms for simulating complex multiscale systems, and their implementation to the analysis and optimization of renewable energy systems.