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Raul F. Tempone

Professor, Applied Mathematics and Computational Science

Principal Investigator, Stochastic Numerics Research Group

Computer, Electrical and Mathematical Science and Engineering Division
Email icon raul.tempone@kaust.edu.sa

Working in mathematics is an adventure. I seek the thrill of discovery—always visiting new places with the mind, searching for simplicity, beauty and truth. Developing theory and problem-solving are essential; keeping applications present gives meaning to our excursions.

Program Affiliations

Biography

Professor Tempone received his Ph.D. in Numerical Analysis ('02) from the Royal Institute of Technology, Sweden. The next phase of his career took him to the United States, where he completed his postdoctoral studies 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, with the rank of Associate Professor of Applied Mathematics before becoming a Full Professor of Applied Mathematics in 2015. He is also the principal investigator of the Stochastics Numerics Research Group at KAUST.

A variety of fields, such as computational mechanics, quantitative finance, biological and chemical modelling 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) and the first Dahlquist Fellowship in Sweden (2007-2008). He was elected Program Director of the SIAM Uncertainty Quantification Activity Group (2013-2014).

Research Interests

Professor Raul 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, Bayesian inverse problems, scientific machine learning, stochastic optimization, 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 applications spanning computational mechanics, quantitative finance, biological and chemical modeling, and wireless communications. His research group is dedicated to tackling a posteriori error approximation, data assimilation, hierarchical and sparse approximation, optimal control, optimal experimental design, and the rigorous analysis of numerical methods.

Professor Tempone's approach is not only theoretical but also highly applicable, seeking to address real-world problems in various domains by leveraging 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.
Keyword tag icon
data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Education Profile

  • Ph.D. Numerical Analysis, Royal Institute of Technology, 2002

  • M.S. Engineering Mathematics, Universidad de la Republica, Montevideo, Uruguay, 1999

  • B.S. Industrial and Mechanical Engineering, Universidad de la Republica, Montevideo, Uruguay, 1995

Awards and Recognitions

  • Thomson Reuters highly cited researcher, 2016

  • the Alexander von Humboldt professorship, RWTH Aachen, 2018

Publications

  • Hoel, H., Shaimerdenova, G., & Tempone, R. (2022). Multi-index ensemble Kalman filtering. Journal of Computational Physics, 111561.

  • Cramer, E., Mitsos, A., Tempone, R., & Dahmen, M. (2022). Principal component density estimation for scenario generation using normalizing flows. Data-Centric Engineering, 3.

  • Kiessling, J., Ström, E., & Tempone, R. (2021). Wind field reconstruction with adaptive random Fourier features. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 477(2255). doi:10.1098/rspa.2021.0236

  • Espath, L., Kabanov, D., Kiessling, J., & Tempone, R. (2021). Statistical learning for fluid flows: Sparse Fourier divergence-free approximations. Physics of Fluids, 33(9), 097108. doi:10.1063/5.0064862

  • Nadhir Ben Rached, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone , On the Efficient Simulation of Outage Probability in a Log-normal Fading Environment,IEEE Transactions on Communications 65 Issue: 6, 2017

  • F. Ruggeri, Z. Sawlan, M. Scavino, R. Tempone, A hierarchical Bayesian setting for an inverse problem in linear parabolic PDEs with noisy boundary conditions, Bayesian Analysis, Advance Publication, 12 May 2016. doi: 10.1214/16-BA1007

  • C. Bayer, J Happola, R. Tempone, Implied Stopping Rules for American Basket Options from Markovian Projection, arXiv:1705.00558v1, May 2017

  • A. Haji-Ali, R. Tempone, "Multilevel and Multi-index Monte Carlo methods for the McKean-Vlasov equation", has been accepted for publication in Statistics and Computing. 20

  • M. Iglesias, Z. Sawlan, M. Scavino, R. Tempone, C. Wood, Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurements, accepted for publication in International Journal of Heat and Mass Transfer, Sep. 2017

Research Areas

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