uncertainty quantification

Omar Knio Research Group

bayesian inference computational fluids mechanics uncertainty quantification

Professor Omar Knio leads the so-called O-Knio research group, which stands for Omar Knio Research Group Research group areas of expertise and current scientific interests: bayesian inference, computational fluids mechanics, uncertainty quatification ‌Led by Prof. Omar Knio, the O-Knio research group. His research interests include uncertainty quantification, Bayesian inference, computational fluid mechanics, combustion, oceanic and atmospheric flows, turbulent flow, physical acoustics, energetic materials, microfluidic devices, dynamical systems, asymptotic techniques, multi-resolution

Saja Almohammadi

Ph.D. Student, Applied Mathematics and Computational Sciences

uncertainty quantification

Saja Almohammadi is a PhD candidate in Applied Mathematics and Computational Sciences. She is studying under supervision of Professor Omar Knio. Research Interest Saja Almohammadi's research interest include working on Uncertainty Quantification (UQ) on chemical system. Development of methods for forward UQ as applied to chemical kinetic application relevant to Rapid compression machine on iso-octane. Education Profile M.Sc. in Applied Math, King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2014. BS Mathematics Department, King Abdelaziz University (KAU), Saudi Arabia

Ricardo Manuel Pinto de Lima

Research Scientist, Applied Mathematics and Computational Sciences

optimization process systems engineering renewable energy energy systems stochastic programming robust optimization uncertainty quantification risk management nlp minlp milp scheduling

My mission is to develop computational models and methods based on mathematical optimization and data science for sustainable energy and chemical processes. I am deeply committed to decarbonization, climate change mitigation, and optimal operations management.

Omar Knio

Professor, Applied Mathematics and Computational Sciences

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.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group

monte carlo algorithms uncertainty quantification multilevel methods data assimilation Inverse problem bayesian inference MCMC

Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.