Applied Probability and Uncertainty Quantification

Omar Knio received his Ph.D. in mechanical engineering in 1990 from the Massachusetts Institute of Technology (MIT) in the United States. He held a postdoctoral associate position at MIT before joining the mechanical engineering faculty at Johns Hopkins University in 1991. In 2011, he joined the Department of Mechanical Engineering and Materials Science at Duke University, where he also served as associate director of the Center for Material Genomics. In 2012, he was named the Edmund T. Pratt, Jr. Professor of Mechanical Engineering and Materials Science at Duke.

In 2013, Knio joined the Applied Mathematics and Computational Sciences (AMCS) Program at KAUST, where he also served as deputy director of the SRI Center for Uncertainty Quantification in Computational Science and Engineering and as the interim dean of the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. In 2024, he was appointed associate vice president of National Partnerships, Engagement and Academic Liaison, at the KAUST National Transformation Institute.

He is a founding associate editor of the SIAM/ASA Journal on Uncertainty Quantification and currently serves on the editorial boards of the International Journal for Uncertainty Quantification and Theoretical and Computational Fluid Dynamics.

Knio has received several awards, including the Associated Western Universities Faculty Fellowship Award in 1996, the Friedrich Wilhelm Bessel Award in 2003, the R&D 100 Award in 2005, the Distinguished Alumnus Award from the American University of Beirut in 2005, and the Abdul-Hameed Shoman Award for Arab Researchers in 2019.

Professor Knio’s research interests include uncertainty quantification, Bayesian inference, combustion, oceanic and atmospheric flows, physical acoustics, energetic materials, microfluidic devices, renewable energy systems, high-performance computing, optimization under uncertainty, and data-enabled predictive science.

Professor Tempone received his Ph.D. in numerical analysis in 2002 from the Royal Institute of Technology, Sweden. The next phase of his career took him to the United States, where he completed his postdoc at the University of Texas Institute for Computational and Engineering Sciences (ICES), before joining Florida State University as an assistant professor of mathematics.

 
Tempone joined KAUST in 2009 as a founding faculty member, as an associate professor of applied mathematics, and became a full professor in 2015. He is also principal investigator of the Stochastics Numerics Research Group.

 
A variety of fields, such as computational mechanics, quantitative finance, biological and chemical modeling, and wireless communications, are driving his research. More specifically, his research contributions include a posteriori error approximation and related adaptive algorithms for numerical solutions to deterministic and stochastic differential equations. His honors include the German Alexander von Humboldt Professorship (2018–2025), the first Dahlquist Fellowship in Sweden (2007–2008), and being elected program director of the SIAM Uncertainty Quantification Activity Group (2013–2014).  

Tempone's expertise and research interests lie at the intersection of applied mathematics, computational science, and stochastic analysis, with a strong focus on developing and analyzing numerical methods for stochastic and deterministic problems. His work emphasizes adaptive algorithms and hierarchical and sparse approximation, Bayesian inverse problems and data assimilation, optimal experimental design, scientific machine learning, stochastic optimization, optimal control, and uncertainty quantification, aiming to push the boundaries of computational efficiency and accuracy in simulations.

At the helm of the Stochastic Numerics Research Group at KAUST, Tempone is particularly interested in the development and analysis of numerical methods to advance applications spanning computational mechanics, quantitative finance, renewable energy sources management, biological and chemical modeling, and wireless communications.

His approach is theoretical and highly applicable, addressing real-world problems across various domains while grounded in solid foundations of mathematical and computational techniques. His work is instrumental for those interested in the practical application of mathematics to solve complex, real-world issues, making his research group an ideal place for potential collaborators, postgraduate students, postdocs, and research scientists looking for cutting-edge projects at the nexus of uncertainty quantification and computational science.