uncertainty quantification

Abdul-Lateef Haji-Ali

Ph.D., Applied Mathematics and Computational Science

uncertainty quantification numerical analysis Multilevel Monte Carlo

Shyam Mohan Subbiah Pillai

Research Consultant, Stochastic Numerics Research Group

numerical analysis uncertainty quantification stochastic differential equations

Nabila Bounceur

Research Scientist, Stochastic Numerics Research Group

probabilistic sensitivity analysis statistical emulators uncertainty quantification complex system modeling

My work focuses on the modeling of complex systems from the Probabilistic Sensitivity Analysis (PSA) and Uncertainty Quantification (UQ) perspective. I develop methodologies to efficiently characterize input-output relationships in computationally expensive simulators, enabling both qualitative understanding (which factors matter most?) and quantitative assessment (how much does each factor contribute to output uncertainty?).

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

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.

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