Profiles

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.  

• Ph.D., Applied Mathematics (Numerical Analysis), KTH Royal Institute of Technology, Stockholm, Sweden, 2021
• M.S., Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden, 2014
• B.S., Microelectronics, KTH Royal Institute of Technology, Stockholm, Sweden, 2012

Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, PINNs, unsupervised learning, and Numerical Analysis.

Erik von Schwerin's research interests include Deterministic and stochastic differential equations, Computations with uncertainty, Error control and adaptivity, Systematic coarse graining, Stochastic optimal control, Hybrid modeling, and Multiscale methods.

Nabila Bounceur received the Dipl. Eng. degree in Automatic from the National Polytechnic School in Algeria in 2005, the M.Sc. degree in didactical teaching of mathematics from the University of Namur in Belgium in 2008, and the Ph.D. degree in sciences from the Université catholique de Louvain in Belgium in 2015. She started her academic career in 2016 as a postdoc at King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, where she is currently a Research Scientist at the Division of Computer, Electrical and Mathematical Science and Engineering (CEMSE), in the Stochastic Numerics Research Group (StochNum). In academia, she worked extensively on the application of Bayesian modeling approaches and developing frameworks for understanding complex systems.

Selected Publications

• Arias Ortiz, D., Bounceur, N., Patzek, T.W. (2022). Validation and Analysis of the Physics-Based Scaling Curve Method for Ultimate Recovery Prediction in Hydraulically Fractured Shale Gas Wells. SPE Annual Technical Conference and Exhibition (ATCE). OnePetro. doi:10.2118/210191-MS
• Lord, N.S., Crucifix, M., Lunt, D.J., Thorne, M.C., Bounceur, N., et al. (2017). Emulation of long-term changes in global climate: application to the late Pliocene and future. Climate of the Past, 13, 1539–1571. doi:10.5194/cp-13-1539-2017
• Bounceur, N., Crucifix, M., Wilkinson, R.D. (2015). Global sensitivity analysis of the climate-vegetation system to astronomical forcing: an emulator-based approach. Earth System Dynamics, 6, 205–224. doi:10.5194/esd-6-205-2015

Nabila’s research interests include Probabilistic Sensitivity Analysis, Uncertainty Quantification, Gaussian Process Emulation, Bayesian Modeling, Computer Experiments and Space-Filling Designs, Data Reduction and Feature Extraction, and applications to Climate, Environment, and Energy systems.

Arved Bartuska obtained his bachelor's and master's degrees at the University of Vienna. He received his Ph.D. in 2025 at RWTH Aachen University and is currently a postdoctoral fellow at KAUST.

Arved Bartuska's research interests include applied mathematics, stochastic analysis, Bayesian optimal experimental design, and uncertainty quantification.

Yang Liu is a Postdoctoral Fellow at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). His primary research interests involve uncertainty quantification, Monte Carlo methods, and finite element methods.

Abderrahmene Ben Romdhane is an Ms/PhD Student in the Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raúl F. Tempone at King Abdullah University of Science and Technology (KAUST).

Education Profile

• Master of Science in Applied Mathematics and Computational Sciences, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, (January 2026 - Present).
• National Engineering Diploma in Multidisciplinary Engineering, École Polytechnique de Tunisie, Tunis, Tunisia (September 2022 - September 2025).|
• Undergraduate Degree in Maths and Physics, Preparatory Institute for Engineering Studies of el Manar, Tunis, Tunisia (September 2020 - June 2022).

Early Career

• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (October 2025 - December 2025)
• Visiting Student in Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, (February 2025- August 2025).

Abderrahmene's research interests include Numerical Analysis, Computational Finance, Uncertainty Quantification.

Amin Wu is an Ph.D. candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), working in Stochastic Numerics Research Group under the supervision of Professor Raul Tempone. Before joining KAUST, Wu obtained a bachelor's degree from the Communication University of China.

Wu's research interests include the Spatio-temporal series and Gaussian processes.

Salim Ksous is an MS/PhD student in the Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raúl F. Tempone at King Abdullah University of Science and Technology (KAUST).

Education Profile

• Master of Science in Applied Mathematics and Computational Sciences, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (January 2026 – Present)
• National Engineering Diploma in Multidisciplinary Engineering, Ecole Polytechnique de Tunisie, Tunis, Tunisia (September 2022 – June 2025)
• Undergraduate Degree in Mathematics and Physics, Preparatory Institute for Engineering Studies of Monastir, Monastir, Tunisia (September 2020 – June 2022)

Early Career and Awards

• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (October 2025 – December 2025)
• Visiting Student in Applied Mathematics, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia (February 2025 – July 2025)
• EPT Scholarship, Ecole Polytechnique de Tunisie, Tunisia (2022 – 2025)

Salim's research interests include optimal control, optimization and numerical methods.

Abdul-Lateef Haji-Ali is an associate professor in the Maxwell Institute for Mathematical Sciences and the School of Mathematical and Computer Sciences, Heriot-Watt University. He obtained his PhD in Applied Mathematics in 2016 from King Abdullah University of Science and Technology (KAUST).

Selected Publications

• A.-L. Haji-Ali, H. Hoel, and R. Tempone​, Weak convergence analysis in the particle limit of the McKean–Vlasov equations using stochastic flows of particle systems, The IMA Journal of Applied Mathematics (2025).​​ DOI: 10.1093/imamat/hxaf015.
• M. B. Giles and A.-L. Haji-Ali. Multilevel Path Branching for Digital Options In: Annals of Applied Probability 34.5 (2024), pp. 4836–4862. ISSN: 1050-5164. DOI: 10.1214/24-AAP2083.
• M. B. Giles and A.-L. Haji-Ali. Multilevel Nested Simulation for Efficient Risk Estimation. In: SIAM/ASA Journal on Uncertainty Quantification 7.2 (Jan. 2019), pp. 497–525. ISSN: 2166-2525. DOI: 10.1137/18m1173186.

Abdul-Lateef's research interests include Uncertainty Quantification, Numerical Analysis, Machine Learning, Stochastic Differential Equations, Numerical methods for SDEs and SPDEs, Multilevel Monte Carlo methods, Particle systems, Crowd modeling, Mean-field theory, Sparse Grids, Combination techniques, Multi-index techniques, and Inverse problems.

I am a PostDoc at King Abdullah University of Science and Technology (KAUST) in Stochastic Processes and Mathematical Statistics Research Group.

I am interested in mathematical statistics, Markov chains, Monte Carlo methods, and stochastic analysis. My research is focused on theoretical and applied problems associated with stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs). The goal is to propose and establish methods that are well-studied theoretically and provide numerical implementations that corroborate the proven theoretical findings and their efficacy.

Markov Chain Monte Carlo, Particle Methods, Stochastic Control, Machine Learning, Stochastic Partial Differential Equations