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Molecular dynamics (MD) is widely employed in both industrial and academic environments, playing a key role in the study of a large variety of systems, ranging fromliquids to solids, as well as biomolecules, such as proteins and nucleic acids (DNA, RNA). MD simulations provide a suitable and convenient method to explore dynamical properties of a system at the atomic level which, in general, aresignificantly difficult and expensive to investigate in experimental settings.

Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

Raul Tempone, Professor, Applied Mathematics and Computational Science

Jan 6, 09:00 - Jan 10, 17:00

B4 B5 A0215

SRI UQ Annual Meeting 6-10 January 2014 See please the program here: schedule Technical presentations slide: here Full Book of Abstracts: here Full Book of Abstracts with Posters: here (40 MB)

SRI UQ Annual Workshop 2013

Raul Tempone, Professor, Applied Mathematics and Computational Science

Apr 26, 09:00 - Apr 30, 17:00

Auditorium L0 Between B4 and B5

Apr 26 2013 9:00 AM - Apr 30 2013 5:00 PM SRI UQ Annual Meeting 26-30 April 2013 Schedule_2013 To view the WORKSHOP report please click on this link: Report

Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)

Raul Tempone, Professor, Applied Mathematics and Computational Science

Jan 6, 09:00 - Jan 9, 17:00

B4 B5 A0215

SRI UQ Annual Meeting 6-9 January 2015. The deadline for poster submission is December 1st, 2014. See the program here Book of Abstracts Book of Abstracts With Posters (63 MB) Technical presentations slides and videos

SIAM Review - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

1 min read · Thu, May 6 2010

uncertainty quantification Smolyak approximation

This work proposes and analyzes a stochastic collocation method for solving elliptic partial differential equations with random coefficients and forcing terms. These input data are assumed to depend on a finite number of random variables.

SIAM J. Numer. Anal. - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

1 min read · Sat, Jun 9 2007

Stochastic PDEs uncertainty quantification

In this paper, we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables.

Dr. Tembine Hamidou was selected to receive the IEEE ComSoc EMEA Outstanding Young Researcher Award

1 min read · Fri, Feb 7 2014

Dr. Tembine Hamidou (KAUST CEMSE Senior Research Scientist, SRI Center for Uncertainty Quantification) was selected to receive the IEEE ComSoc EMEA Outstanding Young Researcher Award for his promising research activities for the benefit of society.

Alexander Litvinenko is giving talk "Sampling and low-rank tensor approximation of the response surface" on workshop "Uncertainty Management for Robust Industrial Design in Aeronautics - UMRIDA", April 15-16, TU Delft, Netherlands

1 min read · Wed, Apr 15 2015

UMRIDA is a Level 1 collaborative project within the European Commission's Seventh Framework Programme. It involves a consortium of 21 partners from the industrial aeronautics sector, leading research institutes and universities as well as SME's.

Prof. Tempone is an invited speaker at the Uncertainty Quantification Workshop Institut Mittag-Leffler, Sweden

1 min read · Mon, May 9 2016

Prof. Tempone is an invited speaker at the Uncertainty Quantification Workshop, from May 9 to 13, 2016. Institut Mittag-Leffler, Sweden.

Alexander Litvinenko, together with colleagues from RWTH Aachen, TU Braunschweig and SkolTech is organizing a minisymposium

1 min read · Mon, Feb 27 2017

data assimilation uncertainty quantification

Alexander Litvinenko, together with colleagues from RWTH Aachen, TU Braunschweig (Germany), and SkolTech (Russia) is organizing a minisymposium "Tensor completion techniques in Data Assimilation, Bayesian Updata and UQ problems" at SIAM CSE Conference being held February 27 through March 3, 2017, at the Hilton Atlanta in Atlanta, Georgia, USA.

Prof. Raul Tempone gave a talk entitled "Multilevel and multi-index sampling methods with applications" in the summer school CEMRACS 2017

1 min read · Fri, Jul 21 2017

Prof. Raul Tempone gave a talk entitled "Multilevel and multi-index sampling methods with applications" in the summer school CEMRACS 2017, Numerical methods for stochastic models: control, uncertainty quantification, mean-field, July 21, 2017, CIRM, Marseille, France.

Professor Raul Tempone awarded Germany’s most endowed research award

1 min read · Tue, May 8 2018

RWTH Aachen uncertainty quantification computational predictions

On May 8, Italian-Uruguayan KAUST Professor Raul Fidel Tempone received the Alexander von Humboldt professorship at RWTH Aachen at a ceremony in Berlin, Germany. The award recognizes Tempone as an international leader in mathematics for uncertainty quantification, a fairly new mathematical specialization that is currently making a breakthrough. Tempone teaches and performs research in the University's Applied Mathematics and Computational Science program.

A new Ms/Ph.D. student joins the team

1 min read · Wed, Feb 1 2023

uncertainty quantification stochastic optimization

In January 2023, student Ons Chaabene joined KAUST through the Stochastic Numerics Research Group, where she will pursue her Ms/Ph.D. studies under Prof. Raul Tempone. StochNum Team wishes an excellent experience for Ons!

Sören Wolfers

Ph.D., Applied Mathematics and Computational Science

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Quan Long

Research Scientist (former), Stochastic Numerics Research Group

Quan Long worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Quan obtained his Ph.D. degree in computational structural mechanics from CSMLab at Cambridge University, where his research focused on isogeometric analysis using subdivision techniques and its applications in large structural deformations, nematic elastomers, and dynamic fractures. During his postdoctoral research, he extended his interests from deterministic problems to stochastic problems, for example, Bayesian

Nathan Quadrio

Visiting Student, Stochastic Numerics Research Group

uncertainty quantification Reservoir Modeling

Nathan Quadrio is a Former visiting student from the Polytechnic University of Turin at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Nathan's research interests include Uncertainty Quantification, Multilevel Monte Carlo, Stochastic Partial Differential Equations, Reservoir Modeling. Selected Publications Nathan Quadrio, "Multilevel Monte Carlo method for PDEs with fluid dynamic applications", Master Thesis, KAUST, October 2014. Education Profile Master's Degree at the Polytechnic University

Marco Ballesio

Ph.D., Applied Mathematics and Computational Science

Stochastic Modeling uncertainty quantification

Marco Ballesio is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Probability Theory and Stochastic Processes, Stochastic Modeling, Uncertainty Quantification. Education Profile Laurea Magistrale (Master’s Degree) in Mathematics for Engineering, Politecnico di Torino (Grade: 110/110 cum laude), (2013-3/2016). Allievi Honors Program, Allievo Senior, Collegio Carlo Alberto (2013-3/2016). Erasmus Student at Aalto

Longting Mo

Visiting Student, Stochastic Numerics Research Group

Longting Mo is a Former visiting student from Nanjing University at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Longting research interest includes Uncertainty quantification in groundwater science. Education Profile Nanjing University. Professional Memberships American Geophysical Union.

Lauri Mustonen

Visiting Student, Stochastic Numerics Research Group

uncertainty quantification numerical methods

Lauri Mustonen is a Former visiting student from Aalto University at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Lauri's research interests include uncertainty Quantification, Inverse Problems, Numerical Methods. Education Profile M.Sc. (Tech.), Engineering Physics, and Mathematics, Aalto University, 2014. Professional Memberships http://math.aalto.fi/en/research/applied/inverseproblems/

Kody J. H. Law

Senior Research Scientist, Stochastic Numerics Research Group

uncertainty quantification data assimilation probability theory

Kody J. H. Law worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Kody specializes in computational approaches to inverse problems, uncertainty quantification, and sequential data assimilation. His interest spans methodology, such as function-space sampling and filtering algorithms, and also applications, such as numerical weather prediction, ocean prediction, climate prediction, and subsurface reconstruction. His interest in data assimilation

Evangelia Kalligiannaki

Research Scientist, Stochastic Numerics Research Group

numerical analysis markov chains uncertainty quantification

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Erik von Schwerin

Research Scientist, Stochastic Numerics Research Group

uncertainty quantification numerical analysis stochastic differential equations Stochastic Optimal Control

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

Carina Suciu

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Carina Suciu worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Carina's research interests included Deterministic and Stochastic Numerical Methods for Partial Differential Equations and Techniques for Predictive Simulations and Uncertainty Quantification. Selected Publications Direct Discretization of Bi-Variate Population Balance Systems with Finite Difference Schemes of Different Order, Chemical Engineering Science 106, 39-52, 2014, joint work with V. John A

Ben Mansour Dia

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Ben Mansour Dia worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Modeling and Numerical Simulations of Incompressible Flows: 2-D shallow water equations, 1-D bilayer shallow water equations. Control Theory: Boundary stabilization and exact controllability of hyperbolic partial differential equations, optimal control of ordinary differential equations and stochastic differential equations Numerical Analysis: Finite Elements Method, Variational Multi-Scale

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical methods uncertainty quantification

André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Alvaro Moraes

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Alvaro Moraes worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Prior to that, Alvaro obtained a Ph.D. degree in Applied Mathematics from KAUST. Research Interests Alvaro's research interests included Uncertainty Quantification, Multiscale Modeling of Epidemic Diseases, and their Statistical Analysis and Simulation and Inference for Pure Jump Processes. Selected Publications A. Moraes, F. Ruggeri, R. Tempone and P. Vilanova, " Multiscale Modeling of Wear Degradation in Cylinder

Alexander Litvinenko

Senior Research Scientist, Stochastic Numerics Research Group

spatio-temporal statistics uncertainty quantification

Alexander Litvinenko worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). After 5 years as a research scientist at KAUST (in various groups: the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification, Extreme Computing Research Center, and Bayesian Computing) Alexander moved to RWTH Aachen to the position Group leader in Uncertainty Quantification. He earned B.S. and M.S. degrees working on data analysis at Sobolev Institute of Mathematics at the

Ting Li

Postdoctoral Research Fellow, Environmental Science and Engineering

remote sensing machine learning precision agriculture agriculture environmental modeling crop health agricultural productivity sustainable agricultural

Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

1 min read · Wed, Feb 18 2026

stochastic algorithms statistical learning machine learning climate and environmental modeling Computational finance

Join us at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2026 for new research on stochastic algorithms, statistical learning, and optimization, with applications in climate modelling and computational finance!

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations

AI machine learning deep learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

Racha Zayni

Visiting Researcher, Information Science Lab

machine learning Data Analytics Wireless Communications AI-based Localization Signal processing

Visiting student at King Abdullah University of Science and Technology (KAUST).

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science

machine learning Electroencephalograms Computer Vision Computational Neuroscience

Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Zakaria El Kassimi

machine learning deep learning Large Language Models

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Jonathan Klein

Research Scientist, Computer Science

machine learning Computational Photography computational simulations

My fields of research interests include computational simulation, plant simulation and modeling, synthetic training data, machine learning architectures, none-line-of-sight imaging, and computational photography.

Yibin Wang

machine learning Free Space Optics FPGA

Keep Moving forward

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

Chengjie Zhao

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

Some Contributions to Particle and Unbiased Simulation Methods

Elsiddig Awadelkarim Elsiddig, Postdoctoral Research Fellow, Statistics

Mar 26, 10:00 - 12:00

B3 L5 R5209; Zoom Meeting 95303621356

Bayesian Statistics machine learning

This thesis develops unbiased estimators and efficient particle filters, leveraging Feynman-Kac formulae and multilevel techniques, to address challenges in parameter estimation, filtering, invariant measure approximation, and stationary distribution computation for partially observed diffusion processes and McKean-Vlasov stochastic differential equations.

Stochastic Numerics and Statistical Learning Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

May 18, 09:00 - May 25, 15:30

Auditorium 0215 between B2 & B3

stochastic algorithm statistical learning machine learning

Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

Research Scientist (former), King Abdullah University of Science and Technology

6g wireless systems cloud- and fog-radio access networks cross-layer optimization Cooperative networks machine learning Optical communications

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow

Computer Vision machine learning AI for healthcare deep learning Medical imaging

Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student

machine learning Computer Vision AI4Science

Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science

Computer Vision machine learning Deep generative models

Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Visiting Scholar (former), King Abdullah University of Science and Technology

Signal processing Wireless Communications machine learning

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting Scholar (former), King Abdullah University of Science and Technology

machine learning neural network

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Researcher (former), King Abdullah University of Science and Technology

machine learning software artificial intelligence

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

M.S. Student, Information Science Lab

Localization machine learning GNSS localization Riemannian Optimization

MS Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

May 21, 08:00 - Jun 1, 17:00

B9

stochastic algorithms statistical learning machine learning

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

May 19, 08:00 - May 30, 17:00

B2 B3 A0215

stochastic algorithms statistical learning machine learning

We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

New preprint available from research on quasi-Newton methods for stochastic optimization

1 min read · Sun, Aug 28 2022

stochastic optimization Bayesian Estimation machine learning

A preprint of a new research project of our group named " Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization" is available at arXiv. The manuscript is authored by André Carlon, Prof. Luis Espath, and Prof. Raúl Tempone. Abstract: Using quasi-Newton methods in stochastic optimization is not a trivial task. In deterministic optimization, these methods are often a common choice due to their excellent performance regardless of the problem's condition number. However, standard quasi-Newton methods fail to extract curvature

Jamer Jimenez Mares

Visiting Student, Stochastic Numerics Research Group

data analysis machine learning

Jamer Jimenez Mares is a Former visiting student from Universidad del Norte, Barranquilla, Atlantico, at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Jamer's research interests include Data analysis, Machine learning, Energy efficiency and Time-series modelling. Selected Publications Towards an intelligent management approach for power consumption in buildings case study, “Computing Conference (CLEI), 2013 XXXIX Latin American” En, vol., no., pp.1,10, 1-5 Oct. 2012 An analysis of

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

MCMC Particle Methods Stochastic Control machine learning stochastic partial differential equations

Aku-Jaakko Alexis Kammonen

Research Scientist, Stochastic Numerics Research Group

numerical methods machine learning

Aku-Jaakko Alexis Kammonen is a Research Scientist at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST).

Lajos Lóczi

Research Scientist, Applied Mathematics and Computational Science

numerical methods PDEs ODEs stability analysis numerical discretization qualitative theory numerical analysis

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

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

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

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

solid mechanics biomechanics numerical analysis scientific computing

Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

Hierarchical ensemble-based data assimilation and rare event tracking

Gaukhar Shaimerdenova, Research Consultant, Stochastic Numerics Research Group

May 15, 10:00 - 12:00

B4 L5 R5220

This thesis consists of three papers considering, in general, two challenges: the minimization of computational cost in the ensemble Kalman filtering (EnKF) method and the problem of tracking a rare event within the framework of the EnKF.

Yang Liu

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Education Profile B.Eng. Aerospace Engineering, Beihang University, Beijing, China, 2017. M.Sc. Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2019. Ph.D. Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2024. Research Interests Yang's research interests include Probability Theory and Stochastic Processes, Numerical Analysis.

Waleed Alhaddad

M.S., Applied Mathematics and Computational Science

Mathematical modeling numerical analysis

Waleed Alhaddad obtained his Master's degree in Applied Mathematics and Computational Sciences from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Raul F. Tempone at Stochastic Numerics Research Group (STOCHNUM). Research Interests Waleed's research interests include Mathematical Modeling, Probability Theory and Stochastic Processes, Numerical Analysis. Education Profile M.Sc. Applied Mathematics, Finance, King Abdullah University of Science and Technology (KAUST) 2016-2018. B.Sc. Physics, University of California Santa Barbara 2016. Awards and

Sören Wolfers

Ph.D., Applied Mathematics and Computational Science

Sören Wolfers obtained a Ph.D. degree in Applied Mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Sören's research interests included Numerical Analysis, Uncertainty Quantification, Sparse Approximation, Multilevel methods and Meshfree methods. Selected Publications Beck, J., Roberts, G., and Wolfers, S. (2018). Bayesian earthquake dating and seismic hazard assessment using chlorine-36 measurements (BED v1). Submitted to

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Mohammad Motamed

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis scientific computing

Mohammad Motamed worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Mohammad is a visiting researcher at ICES, The University of Texas at Austin, USA. Research Interests Mohammad's research interests included Numerical Analysis and Scientific Computing, Deterministic and stochastic partial differential equations, Multiscale problems, modeling and simulation, and high-frequency wave propagation problems. Selected Publications M. Motamed and F. Nobile and R. Tempone. A

Julio Enrique Castrillon Candas

Research Scientist, Stochastic Numerics Research Group

numerical analysis Partial Differential Equations

Julio Enrique Castrillon Candas worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Julio's research focused on the development of fast and accurate numerical methods for the efficient solution of deterministic and stochastic computational models. In particular, his interests included, but not limited, to Stochastic PDEs, Radial Basis Function Interpolation, Regression, Hierarchical Basis, and Probability Theory and Stochastic Processes Selected Publications Julio E

Jesper Karlsson

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis computing

Jesper Karlsson worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Jesper's research interests included Adaptivity and error control for the approximation of deterministic and stochastic differential equations, Optimal control for deterministic and stochastic differential equations, Numerical analysis, and Large scale/High capacity computing. Education Profile 2003-2008 Doctorate, Department of Numerical Analysis, Royal Institute of Technology (KTH), Stockholm

Håkon A. Hoel

Visiting Researcher, Stochastic Numerics Research Group

numerical analysis Mathematical modeling data assimilation

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Georgios Zouraris

Visiting Scholar, Stochastic Numerics Research Group

Georgios Zouraris is a Former Visiting Associate Professor from the University of Crete, Greece at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Georgios' research focuses on the development and the analysis of numerical methods to approximate the solution of stochastic and deterministic differential equations. Selected Publications D. C. Antonopoulou, G. D. Karali, M. Plexousakis and G. E. Zouraris, Crank-Nicolson finite element discretizations for a two-dimensional linear Schrodinger-type

Gaukhar Shaimerdenova

Research Consultant, Stochastic Numerics Research Group

numerical analysis Computational finance

Gaukhar Shaimerdenova is a consultant at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Gaukhar's research interests include Stochastic Differential Equations, Numerical Analysis, Data Assimilation, Multilevel/Multi-index Monte Carlo methods, Ensemble Kalman Filtering and Computational Finance. Selected Publications Rached, N.B., Schwerin, E.V., Shaimerdenova, G. and Tempone, R., 2026. Importance sampling for rare event tracking within the ensemble Kalman filtering framework

Evangelia Kalligiannaki

Research Scientist, Stochastic Numerics Research Group

numerical analysis markov chains uncertainty quantification

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Erik von Schwerin

Research Scientist, Stochastic Numerics Research Group

uncertainty quantification numerical analysis stochastic differential equations Stochastic Optimal Control

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

Bilal Saad

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Bilal Saad worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Numerical analysis Flow and transport in porous media Compressible and incompressible multiphase flows, partially miscible flow Radioactive waste storage, Carbon capture and storage, CO2 storage, Oil recovery secondary Modeling and numerical simulation of a nonlinear degenerate elliptic-parabolic systems Selected Publications B. Saad and M. Saad. Study of full implicit petroleum engineering finite volume

Blanca Ayuso de Dios

Research Scientist, Stochastic Numerics Research Group

Blanca Ayuso de Dios worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Blanca's research was mostly concerned with the development and analysis of numerical methods (mainly finite elements of different types) for Partial Differential Equations. She was also very active in the analysis and development of fast solvers for the algebraic systems arising from the numerical discretizations; in particular domain decomposition and multilevel methods, in the approximation of

Ben Mansour Dia

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Ben Mansour Dia worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Modeling and Numerical Simulations of Incompressible Flows: 2-D shallow water equations, 1-D bilayer shallow water equations. Control Theory: Boundary stabilization and exact controllability of hyperbolic partial differential equations, optimal control of ordinary differential equations and stochastic differential equations Numerical Analysis: Finite Elements Method, Variational Multi-Scale

Agustín Estramil

Visiting Student, Stochastic Numerics Research Group

Agustín Estramil is a Former visiting student from Universidad de la República at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Agustín's research interests include Numerical Analysis; Statistics and Stochastic Differential Equations. Education Profile B.Sc. in Statistics, Universidad de la República (Uruguay), 2017. Thesis Title: Numerical Simulation of the Probabilistic Solution of the Dirichlet Problem.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations

AI machine learning deep learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Zakaria El Kassimi

machine learning deep learning Large Language Models

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Adonai Vera

Intern (former), Image and Video Understanding Lab

Computer Vision Advanced Perception multimodal AI deep learning

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Juan Pablo Muñoz Díaz

Ph.D. Student, Applied Mathematics and Computational Science

applied mathematics bioscience Dynamical Systems deep learning bifurcation theory

Karen Sanchez

Postdoctoral Research Fellow

Computer Vision machine learning AI for healthcare deep learning Medical imaging

Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Mhd. Emad Alolaby

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Sixiu Liu

Ph.D. (former), Earth Science and Engineering

seismic processing deep learning

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab

artificial intelligence TinyML optimization deep learning

Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Dalal E. Aldossary

M.S. Student, Computer Science

machine learning Electroencephalograms Computer Vision Computational Neuroscience

Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Adonai Vera

Intern (former), Image and Video Understanding Lab

Computer Vision Advanced Perception multimodal AI deep learning

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Abdelrahman Eldesokey

Postdoctoral Research Fellow

Computer Vision Diffusion Models Vision Foundation Models Multi-Modal Large Language Models

Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

Deep learning Generative AI and LLMs artificial intelligence Computer Vision

Karen Sanchez

Postdoctoral Research Fellow

Computer Vision machine learning AI for healthcare deep learning Medical imaging

Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Hani A. Al Majed

M.S. Student

machine learning Computer Vision AI4Science

Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science

Computer Vision machine learning Deep generative models

Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Racha Zayni

Visiting Researcher, Information Science Lab

machine learning Data Analytics Wireless Communications AI-based Localization Signal processing

Visiting student at King Abdullah University of Science and Technology (KAUST).

Reem Arnaout

Intern, King Abdullah University of Science and Technology

wireless communication Signal processing Radio Frequency Localization Differential privacy Federated learning Network and Software Security

Visiting student at King Abdullah University of Science and Technology (KAUST).

Serine Medjber

Visiting Student (former), King Abdullah University of Science and Technology

Signal processing embedded systems IoT

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting Student (former), King Abdullah University of Science and Technology

IoT Signal processing wireless communications. embedded systems

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Chengjie Zhao

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab

communication engineering Data detection and decoding coding techniques for Terahertz Massive MIMO Systems Signal processing wireless communication MATLAB Python

Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab

Physically consistent modeling Near-field propagation analysis Signal processing mmWave systems Reconfigurable Intelligent Surfaces Sparse signal recovery 6G

Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

Visiting Scholar (former), King Abdullah University of Science and Technology

Signal processing Wireless Communications machine learning

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Alaa Alameer Ahmad

Visiting Researcher (former), King Abdullah University of Science and Technology

Signal processing communication networks

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Abdullatif Al-Rabah

M.S. (former), Electrical and Computer Engineering

Signal processing communication Network Design

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Salem Al-saqaf

IoT electronics artificial intelligence integrated circuits

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

Deep learning Generative AI and LLMs artificial intelligence Computer Vision

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science

artificial intelligence Generative AI and LLMs

She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Researcher (former), King Abdullah University of Science and Technology

machine learning software artificial intelligence

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab

artificial intelligence TinyML optimization deep learning

Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

Species count mRNA, proteins, dimers - jump processes

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

Sequential Monte Carlo Methods By Prof. Ajay Jasra (National University of Singapore)

Prof. Ajay Jasra, National University of Singapore

Oct 14, 11:00 - 12:30

B1 R4214

This talk will provide a tutorial style introduction to sequential Monte Carlo methods, attempting to start from a basic introduction, to a coverage of some of the latest research developments.

PhD Student Abdul Lateef Haji Ali to Present 'Monte Carlo Methods' Talk at University of Basel

Aug 8, 12:30 - 13:30

KAUST

Monte carlo methods stochastic particle systems Multilevel Monte Carlo

In this talk, I explore the application of single level and multilevel Monte Carlo methods for computing key quantities in a stochastic particle system within the mean-field framework.

Multilevel Approximations of Markovian Jump Processes with Applications in Communication Networks By Pedro Vilanova (PhD Student of Prof. Raul Tempone, KAUST)

Pedro Andres Vilanova Guerra, Postdoctoral Research Fellow, Stochastic Numerics Research Group

May 3, 14:00 - 15:00

Al Marsa Yacht Club

This thesis focuses on the development and analysis of efficient simulation and inference techniques for Markovian pure jump processes with a view towards applications in dense communication networks. The first part of this work proposes novel numerical methods to estimate, in an efficient and accurate way, observables from realizations of Markovian jump processes.

Prof. Tempone is plenary speaker at MCQMC2014

1 min read · Thu, Oct 24 2013

Prof. Tempone is a plenary speaker at the 11th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing.

PhD Student Abdul Lateef Haji Ali to Present 'Monte Carlo Methods' Talk at University of Basel

1 min read · Sat, Aug 15 2015

I discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs).

Prof. Raul Tempone and Dr. Chiheb Ben Hammouda are organizing a minisymposia "Hierarchical Methods for Variance Reduction" at the MCQMC Conference, 10-14 August 2020, University of Oxford

1 min read · Mon, Aug 3 2020

Prof. Raul Tempone and Dr. Chiheb Ben Hammouda are organizing a mini-symposia "Hierarchical Methods for Variance Reduction" at the Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing Conference, 10-14 August 2020, University of Oxford.

Miguel Angel Alvarez Ballesteros

Ph.D. Student, Applied Mathematics and Computational Science

Monte carlo methods Bayesian computational statistics

Miguel Angel Alvarez Ballesteros is a M.S./Ph.D. candidate in the KAUSTStochastic Numerics research group under the supervision of Professor Raul Tempone. Before joining KAUST, Miguel obtained a bachelor's degree in physics from the Universidad Industrial de Santander, Bucaramanga, Colombia. Research Interests Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning. Education Profile Bachelor's degree in Physics from the Universidad Industrial de Santander (UIS), Bucaramanga, Colombia, in 2019. Publications Álvarez, M., Orjuela

Amin Wu successfully defended her PhD Thesis

1 min read · Tue, Mar 3 2026

bayesian inference stochastic processes stochastic differential equations multilevel methods

On March 3, 2026, Amin Wu successfully defended her PhD Thesis entitled "Bayesian Inference for Partially Observed Continuous-Time Processes".

Shyam Mohan Subbiah Pillai

Research Consultant, Stochastic Numerics Research Group

numerical analysis uncertainty quantification stochastic differential equations

AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning

stochastic differential equations Ito integral Monte Carlo Multilevel Monte Carlo Importance sampling Variance Reduction Kolmogorov Backward Equation Fokker-Planck equations Hamilton-Jabobi-Bellman Stochastic Optimal Control

The goal of this course is to give basic knowledge of stochastic differential equations and their numerical solution, useful for scientific and engineering modeling, guided by some problems in applications in financial mathematics, material science, geophysical flow problems, turbulent diffusion, control theory, and Monte Carlo methods.

KAUST alumnus Dr. Abdul-Lateef Haji-Ali receives prestigious Humboldt Research Fellowship for Experienced Researchers

2 min read · Mon, Sep 29 2025

Awards News

uncertainty quantification stochastic differential equations Stochastic Optimal Control

KAUST alumnus Abdul-Lateef Haji-Ali receives Humboldt Fellowship for advances in stochastic modeling

Adaptive Multi Level Monte Carlo (MLMC)

Wed, Jun 1 2011 - Fri, Nov 1 2013

stochastic differential equations

Stochastic differential equations (SDEs), both ordinary time-dependent equations and partial differential equations with random coefficients, are common mathematical tools to model natural processes with uncertainty.

SDEs with irregular coefficients By Prof. Arturo Kohatsu (Ritsumeikan University, Japan)

Prof. Arturo Kohatsu, Ritsumeikan University, Japan

Mar 27, 14:00 - 15:00

B1 R4214

stochastic differential equations

In this talk we will give some results regarding the existence and regularity of densities for stochastic differential equations (sde's) with irregular coefficients starting with Holder coefficients and then bounded and measurable. If time allows we will also discuss issues related to the simulation schemes for such sde's.

Efficient Numerical Methods for Stochastic Differential Equations in Computational Finance by Juho Häppölä

Juho Häppölä, Ph.D., Applied Mathematics and Computational Science

Aug 24, 15:00 - 16:30

B3 R5209

stochastic differential equations

Stochastic Differential Equations (SDE) offer a rich framework to model the probabilistic evolution of the state of a system. Numerical approximation methods are typically needed in evaluating relevant Quantities of Interest arising from such models. In this dissertation, we present novel effective methods for evaluating Quantities of Interest relevant to computational finance when the state of the system is described by an SDE.

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Erik von Schwerin

Research Scientist, Stochastic Numerics Research Group

uncertainty quantification numerical analysis stochastic differential equations Stochastic Optimal Control

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

Muhammad Samiullah

Intern, Cyber Security and Resilience Technology

cybersecurity ML FPGA LLM

Cyber Security, FPGA security, AI/ML security

Omar Rushdi

Visiting Student

Cyber Security, Phishing Detection, LLM

Jalal Saleem

Visiting Student

Cyber Security, LEO Satellite Constellation, Collision Avoidance

Ahmar Husain

Visiting Student

Cyber Security, Malware Analysis, LLM

Abidalrahman Obaidi

Visiting Student

Cybersecurity, Intrusion DetectionIDS, LLMs

Abdullah Alobaidi

Visiting Student

cybersecurity trusted execution environments automotive security V2X blockchain

Cybersecurity, SOC Analysis, SIEM, LLM

Ahmad Didouh

Postdoctoral Research Fellow

Ali Hassan

Instructional Professor, Computer Science

cybersecurity malware

Ali Hassan is an instructional professor in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division at KAUST.

Bayesian Inference for Partially Observed Continuous-Time Processes

Amin Wu, Ph.D. Student, Statistics

Mar 3, 10:00 - 12:00

B5 L5 R5220

McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.

AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning

stochastic differential equations Ito integral Monte Carlo Multilevel Monte Carlo Importance sampling Variance Reduction Kolmogorov Backward Equation Fokker-Planck equations Hamilton-Jabobi-Bellman Stochastic Optimal Control

The goal of this course is to give basic knowledge of stochastic differential equations and their numerical solution, useful for scientific and engineering modeling, guided by some problems in applications in financial mathematics, material science, geophysical flow problems, turbulent diffusion, control theory, and Monte Carlo methods.

Multi-Index Monte Carlo: when sparsity meets sampling by Prof. Raul Tempone

Raul Tempone, Professor, Applied Mathematics and Computational Science

Aug 28, 13:00 - 14:00

B9 R2322 H1

Monte Carlo

Last year I provided a quick glance into the research of my KAUST Stochastic Numerics group, giving particular emphasis on the activities of my KAUST students. There I also connected our KAUST research and our educational activities, including the Numerical Methods for Stochastic Differential Equations (AMCS 336) and Stochastic Methods for Engineers (AMCS 308) KAUST courses.

Chaouki Ben Issaid successfully defended his MS Thesis

1 min read · Mon, May 4 2015

On April 30th, 2015, Chaouki Ben Issaid successfully defended his MS Thesis entitled "Bayesian Optimal Experimental Design Using Multilevel Monte Carlo".

Monte Carlo

A talk by Dr. Chiheb Ben Hammouda in the Monte Carlo & Quasi-Monte Carlo (MCQMC) Methods in Scientific Computing Conference, in August 2020, University of Oxford

1 min read · Tue, Aug 11 2020

Monte Carlo markov chains

Dr. Chiheb Ben Hammouda gave, on August 11, 2020, a talk entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" in the Monte Carlo & Quasi-Monte Carlo (MCQMC) Methods in Scientific Computing Conference, 10-14 August 2020, University of Oxford.

Manuscript accepted by Journal of Computational Physics

1 min read · Mon, Sep 5 2022

Monte Carlo multilevel Multi-index Kalman filter

The manuscript entitled " Multi-index ensemble Kalman filtering" by Håkon A. Hoel, Gaukhar Shaimerdenova, and Raul Tempone has been accepted by the Journal of Computational Physics (JCP). The JCP is a bimonthly scientific journal covering original scientific contributions in advanced mathematical and numerical modeling reflecting a combination of concepts, methods, and principles of physics, mechanics, applied mathematics, statistics, applied geometry, computer science, chemistry, and other scientific disciplines as well. It was established in 1966 and is published by Elsevier. According to

New publication in Quantitative Finance Journal

1 min read · Wed, Nov 2 2022

Adaptive sparse grid quadrature Quasi-Monte Carlo Numerical smoothing Richardson extrapolation option pricing Monte Carlo Distribution functions Greeks Risk estimation

At the end of October, the paper Numerical smoothing with hierarchical adaptive sparse grids and quasi-Monte Carlo methods for efficient option pricing by Christian Bayer ( Weierstrass Institute for Applied Analysis and Stochastics - WIAS) , Chiheb Ben Hammouda ( RWTH Aachen University ) , and Raul Tempone ( King Abdullah University of Science and Technology - KAUST; RWTH Aachen University ), was published in the Quantitative Finance Journal and is available at Taylor & Francis Online. Abstract: When approximating the expectations of a functional of a solution to a stochastic differential

Nadhir Ben Rached

Ph.D., Applied Mathematics and Computational Science

wireless communication Monte Carlo

Introduction Nadhir went to RWTH Aachen University, Aachen, Germany as a Postdoctoral Research Fellow. Education and early career Nadhir obtained his M.S and Ph.D. degree in Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia between 2012 - 2018. Research Interest Efficient Monte Carlo approach for rare event simulation Accurate Performance Analysis of Wireless Communication Systems Computational Finance Stochastic Differential Equations Numerical Analysis Selected Publications Graduation Project, Adaptive Monte

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

Data Assimilation - Part 2

Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group

Feb 20, 15:00 - 17:30

B3 R5520

data assimilation

Please register in advance at: Registration See please the program here: Schedule See lecture notes here: Notes See Melanie Ades notes - Part I: Slides See MATLAB code here: Code See Mealine Ades notes - Part II: Slides See Kody Law notes here: Slides

Data Assimilation - Part 1

Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group

Feb 17, 09:00 - 11:15

B3 R5520

data assimilation

Please register in advance at: Registration See please the program here: Schedule See lecture notes here: Notes See Melanie Ades notes - Part I: Slides See MATLAB code here: Code See Mealine Ades notes - Part II: Slides See Kody Law notes here: Slides

Alexander Litvinenko, together with colleagues from RWTH Aachen, TU Braunschweig and SkolTech is organizing a minisymposium

1 min read · Mon, Feb 27 2017

data assimilation uncertainty quantification

Alexander Litvinenko, together with colleagues from RWTH Aachen, TU Braunschweig (Germany), and SkolTech (Russia) is organizing a minisymposium "Tensor completion techniques in Data Assimilation, Bayesian Updata and UQ problems" at SIAM CSE Conference being held February 27 through March 3, 2017, at the Hilton Atlanta in Atlanta, Georgia, USA.

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Kody J. H. Law

Senior Research Scientist, Stochastic Numerics Research Group

uncertainty quantification data assimilation probability theory

Kody J. H. Law worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Kody specializes in computational approaches to inverse problems, uncertainty quantification, and sequential data assimilation. His interest spans methodology, such as function-space sampling and filtering algorithms, and also applications, such as numerical weather prediction, ocean prediction, climate prediction, and subsurface reconstruction. His interest in data assimilation

Håkon A. Hoel

Visiting Researcher, Stochastic Numerics Research Group

numerical analysis Mathematical modeling data assimilation

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Salem Al-saqaf

IoT electronics artificial intelligence integrated circuits

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Serine Medjber

Visiting Student (former), King Abdullah University of Science and Technology

Signal processing embedded systems IoT

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting Student (former), King Abdullah University of Science and Technology

IoT Signal processing wireless communications. embedded systems

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Ruochen Zhu

human body communication IoT

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

Lajos Lóczi

Research Scientist, Applied Mathematics and Computational Science

numerical methods PDEs ODEs stability analysis numerical discretization qualitative theory numerical analysis

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

Salim Ksous

M.S. Student, Stochastic Numerics Research Group

optimal control numerical methods

Realistic scenario planning for solar power

1 min read · Tue, Nov 25 2025

News Clip News

numerical methods

Placing realistic bounds on solar power scenarios enables smarter grid planning and energy management.

Efficient Numerical Methods for High-Dimensional Approximation Problems

Sören Wolfers, Ph.D., Applied Mathematics and Computational Science

Feb 6, 10:00 - 11:30

B4 L5 R5220

numerical methods

In the field of uncertainty quantification, the effects of parameter uncertainties on scientific simulations may be studied by integrating or approximating a quantity of interest as a function over the parameter space. If this is done numerically, using regular grids with a fixed resolution, the required computational work increases exponentially with respect to the number of uncertain parameters -- a phenomenon known as the curse of dimensionality.

Lauri Mustonen

Visiting Student, Stochastic Numerics Research Group

uncertainty quantification numerical methods

Lauri Mustonen is a Former visiting student from Aalto University at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Lauri's research interests include uncertainty Quantification, Inverse Problems, Numerical Methods. Education Profile M.Sc. (Tech.), Engineering Physics, and Mathematics, Aalto University, 2014. Professional Memberships http://math.aalto.fi/en/research/applied/inverseproblems/

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical methods uncertainty quantification

André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Aku-Jaakko Alexis Kammonen

Research Scientist, Stochastic Numerics Research Group

numerical methods machine learning

Aku-Jaakko Alexis Kammonen is a Research Scientist at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST).

Polina Morozova

M.S. Student, Photonics Laboratory

Photonics and optoelectronics

Md Al Shahriar Shakil

M.S. Student, Photonics Laboratory

Photonics and optoelectronics quantum computing. quantum photonics

Md Al Shahriar Shakil was awarded his Bachelor of Science degree in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology (BUET), where his undergraduate research focused on quantum computing and quantum photonics. After completing his bachelor’s degree, he worked as a Research Assistant at BUET, where he gained experience in spin-based quantum qubits for quantum computing. He is currently an MS/PhD student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where his research focuses on

Hanqi Li

M.S. Student, Photonics Laboratory

Photonics and optoelectronics

Hanqi Li received her Bachelor of Science degree in Electronic Science and Technology from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2025. She was an intern student at the Institute of Semiconductors, Chinese Academy of Sciences, during 2024 and 2025, where she worked on topics related to optoelectronics and silicon photonics. Hanqi is currently an MS/PhD student in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, and a member of the Photonics Laboratory.

Mohammed Obidallah Alotaibi

Ph.D. Student, Photonics Laboratory

Photonics and optoelectronics renewable energy technologies sensing devices

I am Mohammed Obidallah Alotaibi, joining the KAUST Photonics Lab. I hold an M.S. in Electrical Engineering from the University of Central Florida and a B.S. from Umm Al-Qura University. Previously, I served as an Instrument Engineer at Aramco and currently lecture at Taibah University. I am passionate about advancing photonic technologies in energy and sustainability applications, and I look forward to contributing to the lab’s research in optoelectronics and integrated photonic systems. Previous Work Experience: Instrument Engineer, Saudi Aramco (2010–2012) Teaching Assistant & Lecturer

Yan Wang

Ph.D. Student, Photonics Laboratory

Photonics and optoelectronics Ultrawide bandgap materials

M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2024- present BEng. in Communication Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2019 - Jun. 2024

Jiahao Wei

M.S. Student, Photonics Laboratory

Photonics and optoelectronics

Education Jan 2025 - present, M.S./Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Sep 2020 - Jun 2024, B.S. in Optoelectronic Information Science and Engineering at Southern University of Science and Technology (SUSTech), Shenzhen, China.

Zinan Xiao

Ph.D. Student, Photonics Laboratory

sensors Photonics and optoelectronics

Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

Bayesian experimental designs by Prof. Marco Scavino (visiting Faculty at KAUST)

Marco Scavino, Associate Professor in Statistics, Universidad de la República in Montevideo, Uruguay

Oct 23, 12:00 - 13:00

B9 R2322

statistical analysis data analysis Stochastic Modeling

In this talk, I will describe the main features that characterize a Bayesian framework for experimental design. A special emphasis will be devoted to the description of how to measure the information, conveyed by an experiment, about the unknown parameters of the statistical model used to describe the data uncertainty.

KAUST Ph.D. student wins Society for Industrial and Applied Mathematics award

1 min read · Wed, Aug 21 2019

News Spotlight

Stochastic Modeling Quantitative finance Numerical simulation and analysis

Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

Marco Scavino

Visiting Scholar, Stochastic Numerics Research Group

Stochastic Modeling statistics

Marco Scavino is a Former Visiting Associate Professor from Universidad de la República, Uruguay, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Bayesian techniques for uncertainty quantification, statistical methods of experimental design, statistical procedures for model calibration, selection and validation, multivariate data analysis, statistical inference for stochastic processes, and stochastic modeling. Selected Publications A Laplace method for under

Marco Ballesio

Ph.D., Applied Mathematics and Computational Science

Stochastic Modeling uncertainty quantification

Marco Ballesio is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Probability Theory and Stochastic Processes, Stochastic Modeling, Uncertainty Quantification. Education Profile Laurea Magistrale (Master’s Degree) in Mathematics for Engineering, Politecnico di Torino (Grade: 110/110 cum laude), (2013-3/2016). Allievi Honors Program, Allievo Senior, Collegio Carlo Alberto (2013-3/2016). Erasmus Student at Aalto

Fabián Crocce

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Stochastic Modeling probability theory

Fabián Crocce worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Partial Differential Equations, Probability Theory, and Stochastic Processes Distributed Strategic Learning, Stochastic Modeling. Optimal stopping for diffusions, both one-dimensional and multi-dimensional. Representation of the solution by the Green kernel of the process. His research interests also included optimal stopping problems for jump-processes and infinite-horizon problems. Fourier methods

Chiheb Ben Hammouda

Ph.D., Applied Mathematics and Computational Science

Numerical simulation and analysis Quantitative finance Computational biology Stochastic Modeling

Chiheb Ben Hammouda is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic

Amin Wu successfully defended her PhD Thesis

1 min read · Tue, Mar 3 2026

bayesian inference stochastic processes stochastic differential equations multilevel methods

On March 3, 2026, Amin Wu successfully defended her PhD Thesis entitled "Bayesian Inference for Partially Observed Continuous-Time Processes".

Bayesian Inference for Partially Observed Continuous-Time Processes

Amin Wu, Ph.D. Student, Statistics

Mar 3, 10:00 - 12:00

B5 L5 R5220

McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.

Bayesian inference as optimal transportation By Prof. Youssef Marzouk Massachusetts Institute of Technology, USA

Prof. Youssef Marzouk, Massachusetts Institute of Technology, USA

Apr 22, 14:00 - 15:00

B1 R4214

bayesian inference sequential Monte Carlo

Bayesian inference provides a natural framework for quantifying uncertainty in parameter estimates and model predictions, for combining heterogeneous sources of information, and for conditioning sequential predictions on data. Posterior simulation in Bayesian inference often proceeds via Markov chain Monte Carlo (MCMC) or sequential Monte Carlo (SMC), but the associated computational expense and convergence issues can present significant bottlenecks in large-scale or dynamically complex problems.

Post-Doc André Carlon participation at MCQMC2022 in Linz

1 min read · Mon, Aug 1 2022

bayesian inference Stochastic Methods and Algorithms

Between July 17 to 22 of 2022, the 15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing was held in Linz, Austria. A postdoctoral fellow of our group, André Carlon, presented a joint work with Joakim Beck and Prof. Raúl Tempone named "Adaptive stochastic gradient descent for Bayesian optimal experimental design." Abstract: Experiments play a central role in many fields of science. Usually, it is of the interest of the investigators to perform experiments as efficiently as possible. However, finding the optimal design for an experiment can be a

Post-Doc André Carlon participation at UNCECOMP2023 in Athens

1 min read · Sun, Jul 30 2023

bayesian inference Stochastic Methods and Algorithms

A postdoctoral fellow of our group Dr. Andre Carlon participated in the recently concluded 5th international conference on Uncertainty Quantification in Computational Science and Engineering and presented a talk on Adaptive double-loop Monte Carlo gradient estimators for Bayesian optimal experimental design. The conference held at Athens, Greece between 12-14 June, 2023. Abstract: Designing experiments is a challenging task. Models of experiments can be used to improve their design and maximize informativeness. In Bayesian Optimal Experimental Design (OED) with non-linear models, one uses the

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

1 min read · Wed, Feb 18 2026

stochastic algorithms statistical learning machine learning climate and environmental modeling Computational finance

Join us at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2026 for new research on stochastic algorithms, statistical learning, and optimization, with applications in climate modelling and computational finance!

Abderrahmene Ben Romdhane

M.S. Student, Stochastic Numerics Research Group

numerical analysis Computational finance uncertainty quantification

Soumaya Elkantassi

M.S., Applied Mathematics and Computational Science

Stochastic processes Computational finance

Soumaya Elkantassi obtained a Master's degree in Applied mathematics and Computational Sciences under the supervision of Professor Raul F. Tempone's at Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Soumaya's research interests included Statistics, Probability Theory and Stochastic Processes, and Computational Finance. Education Profile M.Sc. in Applied mathematics and Computational Sciences, Statistics and Data Analysis, King Abdullah University of Science and Technology (2015 - 2017). Engineering in Economics and

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Gaukhar Shaimerdenova

Research Consultant, Stochastic Numerics Research Group

numerical analysis Computational finance

Gaukhar Shaimerdenova is a consultant at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Gaukhar's research interests include Stochastic Differential Equations, Numerical Analysis, Data Assimilation, Multilevel/Multi-index Monte Carlo methods, Ensemble Kalman Filtering and Computational Finance. Selected Publications Rached, N.B., Schwerin, E.V., Shaimerdenova, G. and Tempone, R., 2026. Importance sampling for rare event tracking within the ensemble Kalman filtering framework

Alessandro Iania

Visiting Student, Stochastic Numerics Research Group

Mathematical modeling Computational finance

Alessandro Iania is a Former visiting student from Politecnico di Torino at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Alessandro's research interests include Mathematical Modeling and Computational Finance. Selected Publications A. Iania, Basket option pricing for processes with jumps using sparse grids and Fourier transforms, Master Thesis, Politecnico di Torino - KAUST, 2015. Education Profile Bachelor’s degree at Politecnico di Torino.

Abdul-Lateef Haji-Ali

Ph.D., Applied Mathematics and Computational Science

uncertainty quantification numerical analysis Multilevel Monte Carlo

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning

stochastic differential equations Ito integral Monte Carlo Multilevel Monte Carlo Importance sampling Variance Reduction Kolmogorov Backward Equation Fokker-Planck equations Hamilton-Jabobi-Bellman Stochastic Optimal Control

The goal of this course is to give basic knowledge of stochastic differential equations and their numerical solution, useful for scientific and engineering modeling, guided by some problems in applications in financial mathematics, material science, geophysical flow problems, turbulent diffusion, control theory, and Monte Carlo methods.

PhD Student Abdul Lateef Haji Ali to Present 'Monte Carlo Methods' Talk at University of Basel

Aug 8, 12:30 - 13:30

KAUST

Monte carlo methods stochastic particle systems Multilevel Monte Carlo

In this talk, I explore the application of single level and multilevel Monte Carlo methods for computing key quantities in a stochastic particle system within the mean-field framework.

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo By Chaouki ben Issaid (Master Student of Prof. Raul Tempone, KAUST)

Chaouki ben Issaid, Ph.D., Statistics

Apr 30, 11:00 - 12:00

B1 R4214

Multilevel Monte Carlo Laplace approximation

Experimental design can be vital when experiments are resource exhaustive and time-consuming. In this work, we carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions.

Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty

1 min read · Sun, Dec 8 2019

Seismic Wave Propagation Multilevel Monte Carlo

We interpret uncertainty in a model for seismic wave propagation by treating the model parameters as random variables and apply the Multilevel Monte Carlo method to reduce the cost of approximating expected values of selected, physically relevant, quantities of interest (QoI) with respect to the random variables.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

1 min read · Wed, Feb 18 2026

stochastic algorithms statistical learning machine learning climate and environmental modeling Computational finance

Join us at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2026 for new research on stochastic algorithms, statistical learning, and optimization, with applications in climate modelling and computational finance!

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

Christian Bayer

Research Scientist, Weierstrass Institute for Applied Analysis and Stochastics

Quantitative finance stochastic algorithms

Christian Bayer's research focuses on financial mathematics and stochastic numerics, including modelling stock indices and numerical approximation of stochastic optimal control problems.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

May 21, 08:00 - Jun 1, 17:00

B9

stochastic algorithms statistical learning machine learning

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

May 19, 08:00 - May 30, 17:00

B2 B3 A0215

stochastic algorithms statistical learning machine learning

We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

Anwar Alshaibani

Anwar Alshaibani > Appointments, Roles and Affiliations

AI machine learning deep learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Mhd. Emad Alolaby

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Mohammed A Shukri

Consultant (former), King Abdullah University of Science and Technology

Neural Networks AI Python BLE communication

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Tao Ni

Assistant Professor, Computer Science

Cyber Security resilient cyber-physical

Professor Tao Ni (Tony) is focused on building secure, reliable and energy-efficient cyber-physical systems to enable trustworthy computing and confidential sensing in critical infrastructure. His approach combines hardware-software co-design with advanced AI technologies.

Mehdi Zakroum

Postdoctoral Research Fellow, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Web Security Trustworthy AI

Ahmar Husain

Visiting Student, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Malware Analysis LLM

Hassan Hadi

Research Specialist, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security cyber threats SOC UAV Security IDPS

Research Specialist on Cyber Security

Mohamed Ads

Visiting Student (former), King Abdullah University of Science and Technology

IoT Telecommunications Smart Wireless Massive Systems LED Wireless Communication Systems artificial intelligence machine learning Cyber Security

Visiting Student, King Abdullah University of Science and Technology

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Amal Alghamdi

Founder, Impact Alpha, Saudi Arabia

HPC Python data analysis finite element method uncertainty quantification

Nizar Ajeeb

Researcher (former), King Abdullah University of Science and Technology

Programming language MATLAB data analysis Web Technologies

Full Time Researcher Full at King Abdullah University of Science and Technology.

Bayesian experimental designs by Prof. Marco Scavino (visiting Faculty at KAUST)

Marco Scavino, Associate Professor in Statistics, Universidad de la República in Montevideo, Uruguay

Oct 23, 12:00 - 13:00

B9 R2322

statistical analysis data analysis Stochastic Modeling

In this talk, I will describe the main features that characterize a Bayesian framework for experimental design. A special emphasis will be devoted to the description of how to measure the information, conveyed by an experiment, about the unknown parameters of the statistical model used to describe the data uncertainty.

Jamer Jimenez Mares

Visiting Student, Stochastic Numerics Research Group

data analysis machine learning

Jamer Jimenez Mares is a Former visiting student from Universidad del Norte, Barranquilla, Atlantico, at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Jamer's research interests include Data analysis, Machine learning, Energy efficiency and Time-series modelling. Selected Publications Towards an intelligent management approach for power consumption in buildings case study, “Computing Conference (CLEI), 2013 XXXIX Latin American” En, vol., no., pp.1,10, 1-5 Oct. 2012 An analysis of

Changhao He

Next generation wireless communications ISAC UAV-enabled wireless communications machine learning for wireless communication

Yousef Awad

ISAC Unmanned aerial vehicles UAVs reconfigurable intelligent surfaces applications

Bumin Kagan Yildirim

artificial intelligence for wireless communication ISAC distributed multimodal ISAC Semantic Communication

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering

FDA-MIMO Radar Talget Detection ISAC haps remote sensing

Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering

Non-Terrestrial Networks haps ISAC

Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Amal Alghamdi

Founder, Impact Alpha, Saudi Arabia

HPC Python data analysis finite element method uncertainty quantification

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab

communication engineering Data detection and decoding coding techniques for Terahertz Massive MIMO Systems Signal processing wireless communication MATLAB Python

Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Mohammed A Shukri

Consultant (former), King Abdullah University of Science and Technology

Neural Networks AI Python BLE communication

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

New CRG award to develop scalable methods for partially observed stochastic optimal control

1 min read · Sat, Feb 28 2026

award uncertainty quantification Stochastic Optimal Control multilevel methods Computational mathematics

KAUST Stochastic Numerics has been awarded a $900,000 Competitive Research Grant (CRG) for a 36‑month project on efficient hierarchical approximations for partially observed stochastic optimal control, co-led with Prof. Ibrahim Hoteit and including subawards to EPFL (Prof. Fabio Nobile) and WIAS Berlin (Prof. Christian Bayer).

AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning

stochastic differential equations Ito integral Monte Carlo Multilevel Monte Carlo Importance sampling Variance Reduction Kolmogorov Backward Equation Fokker-Planck equations Hamilton-Jabobi-Bellman Stochastic Optimal Control

The goal of this course is to give basic knowledge of stochastic differential equations and their numerical solution, useful for scientific and engineering modeling, guided by some problems in applications in financial mathematics, material science, geophysical flow problems, turbulent diffusion, control theory, and Monte Carlo methods.

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

KAUST alumnus Dr. Abdul-Lateef Haji-Ali receives prestigious Humboldt Research Fellowship for Experienced Researchers

2 min read · Mon, Sep 29 2025

Awards News

uncertainty quantification stochastic differential equations Stochastic Optimal Control

KAUST alumnus Abdul-Lateef Haji-Ali receives Humboldt Fellowship for advances in stochastic modeling

Erik von Schwerin

Research Scientist, Stochastic Numerics Research Group

uncertainty quantification numerical analysis stochastic differential equations Stochastic Optimal Control

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.

Amin Wu successfully defended her PhD Thesis

1 min read · Tue, Mar 3 2026

bayesian inference stochastic processes stochastic differential equations multilevel methods

On March 3, 2026, Amin Wu successfully defended her PhD Thesis entitled "Bayesian Inference for Partially Observed Continuous-Time Processes".

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

Nikita Kondratyev

Research Specialist, Electrical and Computer Engineering

Nonlinear Optics integrated photonics Phased Arrays Numerical Modeling

Mikhail Mishevskii

Nonlinear Optics integrated photonics ultrafast fiber lasers

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aleksandr Movsisian

light–matter interaction integrated photonics Quantum optics quantum metrology optomechanics

Aleksandr received his bachelor’s degree in 2023 in Physics from Moscow State University (MSU, ranked first in Russia) and completed a master’s degree in Physics with honors at the same institution. He has over two years of research experience in quantum optics, optomechanics, and quantum measurement theory as part of the LIGO group at MSU. His work has focused on squeezed states of light, measurement topologies, and methods to overcome the Standard Quantum Limit, resulting in two first-author publications in JOSA B and Optics Letters. Aleksandr also gained hands-on experimental experience at

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering

Nonlinear Optics integrated photonics ultrafast fiber lasers Quantum nanomaterials

Ali O Balubaid

Researcher, King Abdullah University of Science and Technology

living systems Cellular Networks Tissue Engineering bioscience Mathematical modeling

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

Waleed Alhaddad

M.S., Applied Mathematics and Computational Science

Mathematical modeling numerical analysis

Waleed Alhaddad obtained his Master's degree in Applied Mathematics and Computational Sciences from King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Raul F. Tempone at Stochastic Numerics Research Group (STOCHNUM). Research Interests Waleed's research interests include Mathematical Modeling, Probability Theory and Stochastic Processes, Numerical Analysis. Education Profile M.Sc. Applied Mathematics, Finance, King Abdullah University of Science and Technology (KAUST) 2016-2018. B.Sc. Physics, University of California Santa Barbara 2016. Awards and

Håkon A. Hoel

Visiting Researcher, Stochastic Numerics Research Group

numerical analysis Mathematical modeling data assimilation

Håkon Hoel was a visiting researcher in the Stochastic Numerics Group at KAUST. He holds an MSc degree in Computational Science from the University of Oslo (2016) and a Ph.D. in Numerical Analysis from KTH Royal Institute of Technology (2012). His main research interests are numerical analysis of stochastic differential equations, nonlinear filtering, and multilevel Monte Carlo (MLMC) methods. His research interests also include different aspects of implementation and error estimation of stochastic models in the following areas: adaptive weak approximation of stochastic differential equations

Alessandro Iania

Visiting Student, Stochastic Numerics Research Group

Mathematical modeling Computational finance

Alessandro Iania is a Former visiting student from Politecnico di Torino at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Alessandro's research interests include Mathematical Modeling and Computational Finance. Selected Publications A. Iania, Basket option pricing for processes with jumps using sparse grids and Fourier transforms, Master Thesis, Politecnico di Torino - KAUST, 2015. Education Profile Bachelor’s degree at Politecnico di Torino.

Chengjie Zhao

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

Wireless Communications optimization Performance analysis network security

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab

artificial intelligence TinyML optimization deep learning

Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Estimating the expected information gain in Bayesian optimal experimental design

May 2, 12:00 - 13:00

B9 L2 H1

optimization DLMC DLMCIS

Optimal experimental design for parameter estimation is a fast-growing area of research. Let us consider the experimental goal to be the inference of some attributes of a complex system using measurement data of some chosen system responses, and the optimal designs are those that maximize the value of measurement data. The value of data is quantified by the expected information gain utility, which measures the informativeness of an experiment. Often, a mathematical model is used that approximates the relationship between the system responses and the model parameters acting as proxies for the attributes of interest.

Katerina Nik

Assistant Professor, Applied Mathematics and Computational Science

Partial Differential Equations free boundary problems calculus of variations Mathematical Biology Microelectromechanical fluid dynamics mean curvature flow

Professor Nik studies partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering.

PDE with Random Coefficients As A Problem in High-Dimensional Numerical Integration By Prof. Ian H. Sloan (University of New South Wales, Australia)

Prof. Ian H. Sloan, University of New South Wales, Australia

Dec 2, 15:00

B1 R4214

Partial Differential Equations

This talk is concerned with the use of quasi-Monte Carlo methods combined with ﬁnite-element methods to handle an elliptic PDE with a random ﬁeld as a coeﬃcient. A number of groups have considered such problems (under headings such as polynomial chaos, stochastic Galerkin and stochastic collocation) by reformulating them as deterministic problems in a high dimensional parameter space, where the dimensionality comes from the number of random variables needed to characterize the random ﬁeld.

Discontinuous Galerkin approximation to the Vlasov-Poisson system By Dr. Blanca Ayuso de Dios (Centre de Recerca Matematica at Barcelona)

Dr. Blanca Ayuso de Dios, Centre de Recerca Matematica at Barcelona

Nov 25, 15:00 - 16:00

B1 R4214

Partial Differential Equations

One of the simplest model problems in the kinetic theory of plasma--physics is the Vlasov-Poisson system with periodic boundary conditions. Such system describes the evolution of a plasma of charged particles (electrons and ions) under the effects of the transport and self-consistent electric field. In this talk, we present a family of discontinuous Galerkin (DG) methods for the approximation of the Vlasov-Poisson system.

Julio Enrique Castrillon Candas

Research Scientist, Stochastic Numerics Research Group

numerical analysis Partial Differential Equations

Julio Enrique Castrillon Candas worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Julio's research focused on the development of fast and accurate numerical methods for the efficient solution of deterministic and stochastic computational models. In particular, his interests included, but not limited, to Stochastic PDEs, Radial Basis Function Interpolation, Regression, Hierarchical Basis, and Probability Theory and Stochastic Processes Selected Publications Julio E

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

1 min read · Wed, Feb 18 2026

stochastic algorithms statistical learning machine learning climate and environmental modeling Computational finance

Join us at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2026 for new research on stochastic algorithms, statistical learning, and optimization, with applications in climate modelling and computational finance!

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

May 18, 09:00 - May 25, 15:30

Auditorium 0215 between B2 & B3

stochastic algorithm statistical learning machine learning

Explore the latest in stochastic algorithms, statistical learning and optimization at KAUST’s Stochastic Numerics and Statistical Learning Workshop 2025.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

May 21, 08:00 - Jun 1, 17:00

B9

stochastic algorithms statistical learning machine learning

Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

May 19, 08:00 - May 30, 17:00

B2 B3 A0215

stochastic algorithms statistical learning machine learning

We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, CUHK Shenzhen, and Imperial College London, among others.

Yimeng Chen

Postdoctoral Research Fellow, Computer Science

machine learning nlp deep learning statistics computational probability Python

Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Juan José Galeano Arenas

Ph.D. Student, Statistics

statistics

Juan José Galeano Arenas is a MS/PhD student at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Håvard Rue in his research group.

Raphaël Huser’s article among the top downloaded articles in Springer’s Environmental Sciences Journals

1 min read · Tue, Jan 22 2019

INLA statistics xstat research highlights

Luigi Lombardo, Thomas Opitz, and Raphaël Huser’s article “Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster”, published in the scientific journal Stochastic Environmental Research and Risk Assessment (SERRA), in July 2018, is among the top downloaded articles in Springer’s Environmental Sciences Journals for the year 2018. Huser is an Assistant Professor of statistics in the CEMSE division and principal investigator of the Extreme Statistics (extSTAT) Research Group at KAUST.

Marco Scavino

Visiting Scholar, Stochastic Numerics Research Group

Stochastic Modeling statistics

Marco Scavino is a Former Visiting Associate Professor from Universidad de la República, Uruguay, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Bayesian techniques for uncertainty quantification, statistical methods of experimental design, statistical procedures for model calibration, selection and validation, multivariate data analysis, statistical inference for stochastic processes, and stochastic modeling. Selected Publications A Laplace method for under

Reem Arnaout

Intern, King Abdullah University of Science and Technology

wireless communication Signal processing Radio Frequency Localization Differential privacy Federated learning Network and Software Security

Visiting student at King Abdullah University of Science and Technology (KAUST).

Chengjie Zhao

Visiting Researcher, Information Science Lab

wireless communication Signal processing optimization machine learning

Visiting Student, King Abdullah University of Science and Technology

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab

communication engineering Data detection and decoding coding techniques for Terahertz Massive MIMO Systems Signal processing wireless communication MATLAB Python

Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Nadhir Ben Rached

Ph.D., Applied Mathematics and Computational Science

wireless communication Monte Carlo

Introduction Nadhir went to RWTH Aachen University, Aachen, Germany as a Postdoctoral Research Fellow. Education and early career Nadhir obtained his M.S and Ph.D. degree in Applied Mathematics and Computational Science, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia between 2012 - 2018. Research Interest Efficient Monte Carlo approach for rare event simulation Accurate Performance Analysis of Wireless Communication Systems Computational Finance Stochastic Differential Equations Numerical Analysis Selected Publications Graduation Project, Adaptive Monte

Racha Zayni

Visiting Researcher, Information Science Lab

machine learning Data Analytics Wireless Communications AI-based Localization Signal processing

Visiting student at King Abdullah University of Science and Technology (KAUST).

Chaabane Mankai

Wireless Communications optimization Performance analysis network security

Surrounded by bright minds and groundbreaking research, KAUST is a great place to learn and grow.

Saleh AlJazzar

Visiting Scholar (former), King Abdullah University of Science and Technology

Signal processing Wireless Communications machine learning

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Hamidou Tembine

Senior Research Scientist, Stochastic Numerics Research Group

Distributed Strategic Learning Evolutionary Games Wireless Communications

Hamidou Tembine worked as a Center Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Hamidou is a French game theorist and researcher specializing in evolutionary games and co-opetitive mean-field-type games. He is a Global Network Assistant Professor at New York University. He is also the principal investigator and director of the Game Theory and Learning Laboratory (L&G Lab) at New York University. Research Interests Hamidou's research interests included Evolutionary Games, Mean Field

Juan Pablo Muñoz Díaz

Ph.D. Student, Applied Mathematics and Computational Science

applied mathematics bioscience Dynamical Systems deep learning bifurcation theory

Peter Schmid

Professor, Mechanical Engineering

quantitative flow analysis applied mathematics Earth systems aerospace design energy efficiency

Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

The role of applied mathematics in finance

1 min read · Thu, Dec 3 2015

applied mathematics Finance

“Sometimes there’s a strange dichotomy between applied mathematics and pure mathematics,” said Professor Raul Tempone, a founding KAUST faculty member and principal investigator of the University's Stochastic Numerics Research Group. “People think that applied math is simply the application of theory.” The argument is that applied math is just classified by its ends in the sense that one is trying to solve a real problem. Tempone argues that this doesn’t mean that new theories don’t need to be created. It also does not mean that the kinds of problems are less challenging than the ones faced in

Serine Medjber

Visiting Student (former), King Abdullah University of Science and Technology

Signal processing embedded systems IoT

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting Student (former), King Abdullah University of Science and Technology

IoT Signal processing wireless communications. embedded systems

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

embedded systems IoT energy efficiency machine learning artificial intelligence intrusion detection computer networks

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering

FDA-MIMO Radar Talget Detection ISAC haps remote sensing

Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering

Non-Terrestrial Networks haps ISAC

Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022

Hussein Al-Shatri

Research Scientist, Electrical and Computer Engineering

haps nr-u interference management beamforming tochnologies coexistence prototyping

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.

Yanda Meng

Assistant Professor, Bioengineering

artificial intelligence medical image analysis machine learning LLM Large Language Models

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Zakaria El Kassimi

machine learning deep learning Large Language Models

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Yanda Meng designs AI models to improve medical imaging and clinical decision-making.

Ahmar Husain

Visiting Student, Computer, Electrical and Mathematical Sciences and Engineering

Cyber Security Malware Analysis LLM

Muhammad Samiullah

Intern, Cyber Security and Resilience Technology

cybersecurity ML FPGA LLM

Cyber Security, FPGA security, AI/ML security

Bayesian Inference for Partially Observed Continuous-Time Processes

Amin Wu, Ph.D. Student, Statistics

Mar 3, 10:00 - 12:00

B5 L5 R5220

McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

This thesis develops Bayesian inference methods for partially observed stochastic differential equations (SDEs) with unknown parameters, focusing on the stochastic Volterra equation (SVE), non-synchronous diffusions, and McKean-Vlasov SDEs. Employing Euler-Maruyama discretization.

A talk by Dr. Chiheb Ben Hammouda in the Monte Carlo & Quasi-Monte Carlo (MCQMC) Methods in Scientific Computing Conference, in August 2020, University of Oxford

1 min read · Tue, Aug 11 2020

Monte Carlo markov chains

Dr. Chiheb Ben Hammouda gave, on August 11, 2020, a talk entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" in the Monte Carlo & Quasi-Monte Carlo (MCQMC) Methods in Scientific Computing Conference, 10-14 August 2020, University of Oxford.

Evangelia Kalligiannaki

Research Scientist, Stochastic Numerics Research Group

numerical analysis markov chains uncertainty quantification

Evangelia Kalligiannaki worked as a Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Evangelia's research interests concerned the development, analysis, and implementation of model reduction (coarse-graining) techniques for high dimensional particle systems at multiple spaces and time scales, and included studying macro-molecular and stochastic lattice systems. She was particularly interested in the development of effective equilibrium and non-equilibrium coarse-grained models

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab

communication engineering Data detection and decoding coding techniques for Terahertz Massive MIMO Systems Signal processing wireless communication MATLAB Python

Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Mhd. Emad Alolaby

Associate Editor, King Abdullah University of Science and Technology

Network Design telecommunication 5G networks GSM Digital Communications optical fiber AI deep learning MATLAB

Nizar Ajeeb

Researcher (former), King Abdullah University of Science and Technology

Programming language MATLAB data analysis Web Technologies

Full Time Researcher Full at King Abdullah University of Science and Technology.

Nikita Kondratyev

Research Specialist, Electrical and Computer Engineering

Nonlinear Optics integrated photonics Phased Arrays Numerical Modeling

Mikhail Mishevskii

Nonlinear Optics integrated photonics ultrafast fiber lasers

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering

Nonlinear Optics integrated photonics ultrafast fiber lasers Quantum nanomaterials

Lajos Lóczi

Research Scientist, Applied Mathematics and Computational Science

numerical methods PDEs ODEs stability analysis numerical discretization qualitative theory numerical analysis

Lajos Lóczi research focuses on the symbolic aspects of numerical methods for differential equations.

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Juan Pablo Bernal Tamayo

Ph.D., Applied Mathematics and Computational Science

PDEs genomics single cell biology

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

data analysis NumPy PDEs stochastic processes probability theory Python TensorFlow

David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Kody J. H. Law

Senior Research Scientist, Stochastic Numerics Research Group

uncertainty quantification data assimilation probability theory

Kody J. H. Law worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Kody specializes in computational approaches to inverse problems, uncertainty quantification, and sequential data assimilation. His interest spans methodology, such as function-space sampling and filtering algorithms, and also applications, such as numerical weather prediction, ocean prediction, climate prediction, and subsurface reconstruction. His interest in data assimilation

Fabián Crocce

Postdoctoral Research Fellow, Stochastic Numerics Research Group

Stochastic Modeling probability theory

Fabián Crocce worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Partial Differential Equations, Probability Theory, and Stochastic Processes Distributed Strategic Learning, Stochastic Modeling. Optimal stopping for diffusions, both one-dimensional and multi-dimensional. Representation of the solution by the Green kernel of the process. His research interests also included optimal stopping problems for jump-processes and infinite-horizon problems. Fourier methods

Christian Bayer

Research Scientist, Weierstrass Institute for Applied Analysis and Stochastics

Quantitative finance stochastic algorithms

Christian Bayer's research focuses on financial mathematics and stochastic numerics, including modelling stock indices and numerical approximation of stochastic optimal control problems.

KAUST Ph.D. student wins Society for Industrial and Applied Mathematics award

1 min read · Wed, Aug 21 2019

News Spotlight

Stochastic Modeling Quantitative finance Numerical simulation and analysis

Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

Chiheb Ben Hammouda

Ph.D., Applied Mathematics and Computational Science

Numerical simulation and analysis Quantitative finance Computational biology Stochastic Modeling

Chiheb Ben Hammouda is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering

Wireless Underground Sensor Networks LoRAWAN Space-Air-Ground-Underground Integrated Networks Wireless Energy Transfer Reinforcement Learning

Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Mohammed Alwagait

UAV adaptive control Reinforcement Learning Aerial Robotics

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Aznaur Aliev

M.S. Student

AI Deep learning Computer Vision Reinforcement Learning

Ting Li

Postdoctoral Research Fellow, Environmental Science and Engineering

remote sensing machine learning precision agriculture agriculture environmental modeling crop health agricultural productivity sustainable agricultural

Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Gleb Ludnikov

Free-space optical communications remote sensing optical imaging Terahertz communications

To strive and explore, to uncover and never give in.

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering

FDA-MIMO Radar Talget Detection ISAC haps remote sensing

Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Georgios Grekas

Postdoctoral Research Fellow, Applied Mathematics and Computational Science

solid mechanics biomechanics numerical analysis scientific computing

Georgios Grekas research focuses on mathematical modeling (biomechanics, solid mechanics), numerical analysis, scientific and parallel computing, and scientific machine learning.

Steven Dufour

Visiting Scholar, Stochastic Numerics Research Group

Turbulence Multiphase flows scientific computing Finite element methods

Steven Dufour is a Former Visiting Associate Professor from École Polytechnique de Montréal, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Steven's research activities revolve around the development of numerical methodologies for the modeling of free surface flows that can be found in various industrial applications. The main challenge associated with the numerical modeling of multifluid flows is to accurately locate the interfaces between each fluid. More complex flows, such as the flow of

Mohammad Motamed

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis scientific computing

Mohammad Motamed worked as a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Mohammad is a visiting researcher at ICES, The University of Texas at Austin, USA. Research Interests Mohammad's research interests included Numerical Analysis and Scientific Computing, Deterministic and stochastic partial differential equations, Multiscale problems, modeling and simulation, and high-frequency wave propagation problems. Selected Publications M. Motamed and F. Nobile and R. Tempone. A

New preprint available from research on quasi-Newton methods for stochastic optimization

1 min read · Sun, Aug 28 2022

stochastic optimization Bayesian Estimation machine learning

A preprint of a new research project of our group named " Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization" is available at arXiv. The manuscript is authored by André Carlon, Prof. Luis Espath, and Prof. Raúl Tempone. Abstract: Using quasi-Newton methods in stochastic optimization is not a trivial task. In deterministic optimization, these methods are often a common choice due to their excellent performance regardless of the problem's condition number. However, standard quasi-Newton methods fail to extract curvature

A new Ms/Ph.D. student joins the team

1 min read · Wed, Feb 1 2023

uncertainty quantification stochastic optimization

In January 2023, student Ons Chaabene joined KAUST through the Stochastic Numerics Research Group, where she will pursue her Ms/Ph.D. studies under Prof. Raul Tempone. StochNum Team wishes an excellent experience for Ons!

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Nebras Filimban

Ph.D. Candidate, Computer, Electrical and Mathematical Sciences and Engineering

RFICs 6G MIMO beamforming systems

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab

Physically consistent modeling Near-field propagation analysis Signal processing mmWave systems Reconfigurable Intelligent Surfaces Sparse signal recovery 6G

Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Mostafa Kassem

Visiting Student, Cyber Security and Resilience Technology

Automous Vehicles Advanced Perception

Adonai Vera

Intern (former), Image and Video Understanding Lab

Computer Vision Advanced Perception multimodal AI deep learning

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Hani A. Al Majed

M.S. Student

machine learning Computer Vision AI4Science

Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Andres Villa

Postdoctoral Research Fellow

Continual Learning Multimodal learning Visual Language Models AI4Science Action understanding

Andres Villa is a Postdoctoral Fellow in the IVUL lab at King Abdullah University of Science and Technology (KAUST), focusing on AI for Science, Continual Learning, Visual Language Models, and Action Understanding.

Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

Raul Tempone, Professor, Applied Mathematics and Computational Science

Jan 6, 09:00 - Jan 10, 17:00

B4 B5 A0215

SRI UQ Annual Meeting 6-10 January 2014 See please the program here: schedule Technical presentations slide: here Full Book of Abstracts: here Full Book of Abstracts with Posters: here (40 MB)

Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)

Raul Tempone, Professor, Applied Mathematics and Computational Science

Jan 6, 09:00 - Jan 9, 17:00

B4 B5 A0215

SRI UQ Annual Meeting 6-9 January 2015. The deadline for poster submission is December 1st, 2014. See the program here Book of Abstracts Book of Abstracts With Posters (63 MB) Technical presentations slides and videos

Haziq Jamil is a Research Specialist at the King Abdullah University of Science and Technology. His research focuses on statistical theory, methods and computation, with a special inclination towards social science applications.

Miguel Angel Alvarez Ballesteros

Ph.D. Student, Applied Mathematics and Computational Science

Monte carlo methods Bayesian computational statistics

Miguel Angel Alvarez Ballesteros is a M.S./Ph.D. candidate in the KAUSTStochastic Numerics research group under the supervision of Professor Raul Tempone. Before joining KAUST, Miguel obtained a bachelor's degree in physics from the Universidad Industrial de Santander, Bucaramanga, Colombia. Research Interests Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning. Education Profile Bachelor's degree in Physics from the Universidad Industrial de Santander (UIS), Bucaramanga, Colombia, in 2019. Publications Álvarez, M., Orjuela

Smarter MRI image analysis for the whole heart

1 min read · Thu, Jan 22 2026

News Clip News

bayesian analysis Bayesian Statistics

A Bayesian approach that selects the most informative frames for manual labeling could speed up real-time heart MRI analysis.

Some Contributions to Particle and Unbiased Simulation Methods

Elsiddig Awadelkarim Elsiddig, Postdoctoral Research Fellow, Statistics

Mar 26, 10:00 - 12:00

B3 L5 R5209; Zoom Meeting 95303621356

Bayesian Statistics machine learning

This thesis develops unbiased estimators and efficient particle filters, leveraging Feynman-Kac formulae and multilevel techniques, to address challenges in parameter estimation, filtering, invariant measure approximation, and stationary distribution computation for partially observed diffusion processes and McKean-Vlasov stochastic differential equations.

Razan Shams

semiconductor 2D materials biomedical devices

MS/PhD ECE student at KAUST developing Device Physics and MEMS. Experienced in nanofabrication and semiconductor processing. Passionate about applying material science and experimental physics to interdisciplinary challenges in engineering and technology.

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

Saverio Pasqualoni

M.S. Student, Computer Science

collective communication HPC computer networking and systems

Saverio is a Master’s student pursuing graduate studies (M.S. and Ph.D.) in Computer Science at KAUST, working in the SANDS research group on high-performance computing and collective communications, with research interests that bridge systems-level computer science and large-scale simulation through a strong mathematical foundation.

Abdullatif Al-Rabah

M.S. (former), Electrical and Computer Engineering

Signal processing communication Network Design

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).

Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

Chiheb Ben Hammouda, Ph.D., Applied Mathematics and Computational Science

Jul 2, 14:00 - 16:00

KAUST

hierarchical approximation methods option pricing Computational biology

In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.

Chiheb Ben Hammouda

Ph.D., Applied Mathematics and Computational Science

Numerical simulation and analysis Quantitative finance Computational biology Stochastic Modeling

Chiheb Ben Hammouda is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic

Omar Ahmed

M.S. Student, Bioengineering

Computer Vision machine learning Computational Neuroscience Electroencephalograms artificial intelligence

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Dalal E. Aldossary

M.S. Student, Computer Science

machine learning Electroencephalograms Computer Vision Computational Neuroscience

Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Andrea Vanessa Rocha

Research Specialist, Statistics

Statistical Modeling computational statistics machine learning stochastic processes

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Nanbo Li

Postdoctoral Research Fellow, Computer Science

machine learning AI Deep generative models generative ai Computer Vision

Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Ahmad Sait

M.S. Student, Computer Science

Computer Vision machine learning Deep generative models

Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Hassan Hizeh

Digital signal processing Robotics and Control biomedical sensing

Hassan is an MS/PhD electrical engineering student. He is currently based in the CEMSE division at KAUST, working under the supervision of Prof. Tareq Al-Naffouri.

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent

Dalal E. Aldossary

M.S. Student, Computer Science

machine learning Electroencephalograms Computer Vision Computational Neuroscience

Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Yibin Wang

machine learning Free Space Optics FPGA

Keep Moving forward

Muhammad Samiullah

Intern, Cyber Security and Resilience Technology

cybersecurity ML FPGA LLM

Cyber Security, FPGA security, AI/ML security

Abderrahman Kantos

Free-space optical communications quantum computing Quantum information theory

Abderrahman Kantos is currectly a Ph.D. student in the Communication Theory Lab (CTL) under the supervision of Professor Mohamed-Slim Alouini at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before beginning his doctoral studies, Abderrahman completed an internship at RTE (Réseau de Transport d’Électricité), working on quantum computing and the development of quantum algorithms. At École Polytechnique, he participated in the Projet Scientifique Collectif (PSC). His team ranked among the top 10 out of more than 100 teams, under the project title

Gleb Ludnikov

Free-space optical communications remote sensing optical imaging Terahertz communications

To strive and explore, to uncover and never give in.

Najmeddine Dhieb

Research Engineer, Electrical and Computer Engineering

Deep learning Generative AI and LLMs artificial intelligence Computer Vision

Yasmeen Alsaedy

M.S. Student, Computer Science

artificial intelligence Generative AI and LLMs

She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Babar Khan

Ph.D. (former), Bioscience

biotechnology healthcare

Ph.D. degree in King Abdullah University of Science and Technology (KAUST)

Ahsan Mehmood

Intern (former), Information Science Lab

IoT Biomedical Signal Processing healthcare Digital signal processing machine learning

Visiting student at King Abdullah University of Science and Technology (KAUST).

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Amal Alghamdi

Founder, Impact Alpha, Saudi Arabia

HPC Python data analysis finite element method uncertainty quantification

Saverio Pasqualoni

M.S. Student, Computer Science

collective communication HPC computer networking and systems

Saverio is a Master’s student pursuing graduate studies (M.S. and Ph.D.) in Computer Science at KAUST, working in the SANDS research group on high-performance computing and collective communications, with research interests that bridge systems-level computer science and large-scale simulation through a strong mathematical foundation.

AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning

stochastic differential equations Ito integral Monte Carlo Multilevel Monte Carlo Importance sampling Variance Reduction Kolmogorov Backward Equation Fokker-Planck equations Hamilton-Jabobi-Bellman Stochastic Optimal Control

The goal of this course is to give basic knowledge of stochastic differential equations and their numerical solution, useful for scientific and engineering modeling, guided by some problems in applications in financial mathematics, material science, geophysical flow problems, turbulent diffusion, control theory, and Monte Carlo methods.

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

Multilevel ensemble Kalman filtering

Thu, Jan 1 - Mon, Jun 1 2015

Kalman filter

Filtering is a method for sequentially estimating the state of an evolving dynamical system in settings where only partial and possibly inaccurate measurements of the history of the state are available.

Manuscript accepted by Journal of Computational Physics

1 min read · Mon, Sep 5 2022

Monte Carlo multilevel Multi-index Kalman filter

The manuscript entitled " Multi-index ensemble Kalman filtering" by Håkon A. Hoel, Gaukhar Shaimerdenova, and Raul Tempone has been accepted by the Journal of Computational Physics (JCP). The JCP is a bimonthly scientific journal covering original scientific contributions in advanced mathematical and numerical modeling reflecting a combination of concepts, methods, and principles of physics, mechanics, applied mathematics, statistics, applied geometry, computer science, chemistry, and other scientific disciplines as well. It was established in 1966 and is published by Elsevier. According to

Bayesian Optimal Experimental Design Using Multilevel Monte Carlo By Chaouki ben Issaid (Master Student of Prof. Raul Tempone, KAUST)

Chaouki ben Issaid, Ph.D., Statistics

Apr 30, 11:00 - 12:00

B1 R4214

Multilevel Monte Carlo Laplace approximation

Experimental design can be vital when experiments are resource exhaustive and time-consuming. In this work, we carry out experimental design in the Bayesian framework. To measure the amount of information, which can be extracted from the data in an experiment, we use the expected information gain as the utility function, which specifically is the expected logarithmic ratio between the posterior and prior distributions.

Bayesian OED for core-flooding experiment application based on Laplace Approximation by Longting Mo (Visiting Master student, Nanjing University, China)

Longting Mo, Visiting Student, Stochastic Numerics Research Group

Jan 22, 16:00 - 17:00

B1 R4214

Laplace approximation

Core flooding experiments are always needed to be conducted before the application of Enhanced Oil Production (EOR) in the field, which is a great way to improve the oil recovery. In order to optimize existing resources and obtain the information efficiently, it is necessary to optimize these experiments.

Muhanned Alsaif

M.S. Student (former), Electrical and Computer Engineering

communication systems technology advancements. Localization

Young professional with a robust background in research and engineering, specializing in communication and signal processing applications for estimation. Previous experience: positioning using GNSS. Current experience: subsurface formation estimation using downhole and surface logs.

Abdulkarem Badiea Alshaibani

M.S. Student, Information Science Lab

Localization machine learning GNSS localization Riemannian Optimization

MS Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Changhao He

Next generation wireless communications ISAC UAV-enabled wireless communications machine learning for wireless communication

Khalid Kanaan

machine learning for wireless communication Massive MIMO multimodal sensing

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

Manuscript accepted by Journal of Computational Physics

1 min read · Mon, Sep 5 2022

Monte Carlo multilevel Multi-index Kalman filter

The manuscript entitled " Multi-index ensemble Kalman filtering" by Håkon A. Hoel, Gaukhar Shaimerdenova, and Raul Tempone has been accepted by the Journal of Computational Physics (JCP). The JCP is a bimonthly scientific journal covering original scientific contributions in advanced mathematical and numerical modeling reflecting a combination of concepts, methods, and principles of physics, mechanics, applied mathematics, statistics, applied geometry, computer science, chemistry, and other scientific disciplines as well. It was established in 1966 and is published by Elsevier. According to

Amin Wu successfully defended her PhD Thesis

1 min read · Tue, Mar 3 2026

bayesian inference stochastic processes stochastic differential equations multilevel methods

On March 3, 2026, Amin Wu successfully defended her PhD Thesis entitled "Bayesian Inference for Partially Observed Continuous-Time Processes".

New CRG award to develop scalable methods for partially observed stochastic optimal control

1 min read · Sat, Feb 28 2026

award uncertainty quantification Stochastic Optimal Control multilevel methods Computational mathematics

KAUST Stochastic Numerics has been awarded a $900,000 Competitive Research Grant (CRG) for a 36‑month project on efficient hierarchical approximations for partially observed stochastic optimal control, co-led with Prof. Ibrahim Hoteit and including subawards to EPFL (Prof. Fabio Nobile) and WIAS Berlin (Prof. Christian Bayer).

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering

Non-Terrestrial Networks haps ISAC

Xiaowei Pang is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022

Houcine Chougrani

Research Scientist, Electrical and Computer Engineering

Non-Terrestrial Networks Embedded systems.

I decided to join KAUST because of its outstanding environment and the exceptional quality of its professors, postdocs, and students. The university's recent focus on applied research resonates deeply with my background, which spans both academic and industrial domains. This alignment presents a unique opportunity to transform our theoretical concepts into tangible innovations, ultimately contributing to the advancement of society.

KAUST Ph.D. student wins Society for Industrial and Applied Mathematics award

1 min read · Wed, Aug 21 2019

News Spotlight

Stochastic Modeling Quantitative finance Numerical simulation and analysis

Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

Chiheb Ben Hammouda

Ph.D., Applied Mathematics and Computational Science

Numerical simulation and analysis Quantitative finance Computational biology Stochastic Modeling

Chiheb Ben Hammouda is a Ph.D. candidate at Stochastic Numerics Research Group (STOCHNUM) under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Arved Bartuska

Postdoctoral Research Fellow, Stochastic Numerics Research Group

numerical analysis uncertainty quantification optimal experimental design

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is a worldwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.

Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

Chiheb Ben Hammouda, Ph.D., Applied Mathematics and Computational Science

Jul 2, 14:00 - 16:00

KAUST

hierarchical approximation methods option pricing Computational biology

In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.

New publication in Quantitative Finance Journal

1 min read · Wed, Nov 2 2022

Adaptive sparse grid quadrature Quasi-Monte Carlo Numerical smoothing Richardson extrapolation option pricing Monte Carlo Distribution functions Greeks Risk estimation

At the end of October, the paper Numerical smoothing with hierarchical adaptive sparse grids and quasi-Monte Carlo methods for efficient option pricing by Christian Bayer ( Weierstrass Institute for Applied Analysis and Stochastics - WIAS) , Chiheb Ben Hammouda ( RWTH Aachen University ) , and Raul Tempone ( King Abdullah University of Science and Technology - KAUST; RWTH Aachen University ), was published in the Quantitative Finance Journal and is available at Taylor & Francis Online. Abstract: When approximating the expectations of a functional of a solution to a stochastic differential

Ting Li is a Postdoctoral Research Fellow focusing her research on bridging the gap between complex data and real-world agricultural impact.

Abdullah Bukhamsin

Ph.D. Student (former), Bioengineering

MEMS Plant Wearables microfluidics precision agriculture Single atom catalysts

Selected Publications: Bukhamsin, Abdullah, et al. " In vivo dynamics of indole-and phenol-derived plant hormones: Long-term, continuous, and minimally invasive phytohormone sensor." (2025) Science Advances. Bukhamsin, Abdullah, et al. " Accelerating adoption of species-agnostic plant sensors for precision farming." (2025) Nature Reviews Electrical Engineering. Bukhamsin, Abdullah, et al. " Early and high-throughput plant diagnostics: strategies for disease detection." (2024) Trends in Plant Science. Bukhamsin, Abdullah, et al. " Minimally-invasive, real-time, non-destructive, species

Nizar Ajeeb

Researcher (former), King Abdullah University of Science and Technology

Programming language MATLAB data analysis Web Technologies

Full Time Researcher Full at King Abdullah University of Science and Technology.

Houcem Ben Salem

Intern (former), Information Science Lab

mathematics modeling Programming language

Research Intern at KAUST(King Abdullah University of Science and Technology) .

Mutibah Alanazi

Visiting Researcher, Electrical and Computer Engineering

Integrated Photonics technology quantum communication

Mutibah’s research focuses on quantum optics, optoelectronics, and nanophononics, with an emphasis on developing scalable quantum optical technologies. During her PhD at the University of Oxford, she explored quantum coherent emission, polariton condensation, and photon bunching in low-dimensional systems. She has extensive expertise in advanced laser systems, cryogenic techniques, microcavity systems, and material modeling, contributing to several notable publications in the field of condensed matter physics. Research interests Mutibah's research focuses on developing cavity-coupled spin

Ibtihal Alharbi

M.S. Student, Applied Physics

optical communication quantum communication

Ibtihal received her bachelor's degree in 2024 in physics from Taibah university with a GPA of 4.93 / 5 , she is currently working towards a master’s degree under the supervision of Prof. Yating Wan at King Abdullah University of Science and Technology (KAUST). Research interests Ibtihal’s research interests are in the application of integrated photonics for optical and quantum communications. Education profile B.Sc. : Taibah university 2020 - 2024

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

New publication in Quantitative Finance Journal

1 min read · Wed, Nov 2 2022

Adaptive sparse grid quadrature Quasi-Monte Carlo Numerical smoothing Richardson extrapolation option pricing Monte Carlo Distribution functions Greeks Risk estimation

At the end of October, the paper Numerical smoothing with hierarchical adaptive sparse grids and quasi-Monte Carlo methods for efficient option pricing by Christian Bayer ( Weierstrass Institute for Applied Analysis and Stochastics - WIAS) , Chiheb Ben Hammouda ( RWTH Aachen University ) , and Raul Tempone ( King Abdullah University of Science and Technology - KAUST; RWTH Aachen University ), was published in the Quantitative Finance Journal and is available at Taylor & Francis Online. Abstract: When approximating the expectations of a functional of a solution to a stochastic differential

Amin Wu

Ph.D. Student, Statistics

spatio-temporal statistics Gaussian processes

Alexander Litvinenko

Senior Research Scientist, Stochastic Numerics Research Group

spatio-temporal statistics uncertainty quantification

Alexander Litvinenko worked as a Senior Research Scientist at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). After 5 years as a research scientist at KAUST (in various groups: the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification, Extreme Computing Research Center, and Bayesian Computing) Alexander moved to RWTH Aachen to the position Group leader in Uncertainty Quantification. He earned B.S. and M.S. degrees working on data analysis at Sobolev Institute of Mathematics at the

Post-Doc André Carlon participation at MCQMC2022 in Linz

1 min read · Mon, Aug 1 2022

bayesian inference Stochastic Methods and Algorithms

Between July 17 to 22 of 2022, the 15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing was held in Linz, Austria. A postdoctoral fellow of our group, André Carlon, presented a joint work with Joakim Beck and Prof. Raúl Tempone named "Adaptive stochastic gradient descent for Bayesian optimal experimental design." Abstract: Experiments play a central role in many fields of science. Usually, it is of the interest of the investigators to perform experiments as efficiently as possible. However, finding the optimal design for an experiment can be a

Post-Doc André Carlon participation at UNCECOMP2023 in Athens

1 min read · Sun, Jul 30 2023

bayesian inference Stochastic Methods and Algorithms

A postdoctoral fellow of our group Dr. Andre Carlon participated in the recently concluded 5th international conference on Uncertainty Quantification in Computational Science and Engineering and presented a talk on Adaptive double-loop Monte Carlo gradient estimators for Bayesian optimal experimental design. The conference held at Athens, Greece between 12-14 June, 2023. Abstract: Designing experiments is a challenging task. Models of experiments can be used to improve their design and maximize informativeness. In Bayesian Optimal Experimental Design (OED) with non-linear models, one uses the

Simon Nik

Postdoctoral Research Fellow, Statistics

Statistics of extremes extreme-value theory stochastic partial differential equations discrete time series

Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany, with a thesis entitled " Stochastic modeling and statistical analyses of non-negative data and times series". He is now a postdoctoral researcher at the King Abdullah University of Science and Technology (KAUST), working in the XSTAT group under Prof. Raphaël Huser's supervision. Education and Early Career Simon obtained his PhD in 2024 from Helmut-Schmidt-University Hamburg, Germany. He joined KAUST as a postdoc in December 2024. Research interests Simon's current research interests focus primarily on extreme

Elsiddig Awadelkarim Elsiddig

Postdoctoral Research Fellow, Statistics

MCMC Particle Methods Stochastic Control machine learning stochastic partial differential equations

SIAM J. Numer. Anal. - A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

1 min read · Sat, Jun 9 2007

Stochastic PDEs uncertainty quantification

In this paper, we propose and analyze a stochastic collocation method to solve elliptic partial differential equations with random coefficients and forcing terms (input data of the model). The input data are assumed to depend on a finite number of random variables.

Dr. A. Litvinenko together with colleagues from France and Germany is organizing a minisymposia at Congress on Industrial and Applied Mathematics (ICIAM2015), Aug. 10-14, 2015 in Beijing, China

1 min read · Mon, Aug 10 2015

Approximations of stochastic and multi-parametric differential equations may lead to extremely high dimensional problems that suffer from the so called curse of dimensionality. Computational tractability may be recovered by relying on adaptive low-rank/sparse approximation.

Stochastic PDEs

Mikhail Mishevskii