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<> uncertainty quantification (29)

Uncertainty Quantification in Molecular Dynamics By Dr. Francesco Rizzi (John Hopkins University, USA)

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B1 R3119

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)

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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

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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)

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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 ·
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

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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

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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 ·
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

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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 ·
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 ·
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 ·
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 ·
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

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 Raul Tempone is an worlwide 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

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

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

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

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

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
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

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

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
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
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

Aku Jaakko Alexis Kammonen

Aku Jaakko Alexis Kammonen 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 Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]
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Andrea Vanessa Rocha

Research Specialist, Statistics
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

David Gomez-Cabrero

Associate Professor, Bioscience
Professor Cabrero's research focuses on developing statistics and machine learning tools for understanding biological systems that address health-related applications, advance biomedical research and enhance our understanding of the molecular mechanisms of diseases with the potential to uncover novel therapeutic approaches.

Some Contributions to Particle and Unbiased Simulation Methods

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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

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Auditorium 0215 between B2 & B3

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

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Hayssam Dahrouj

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

Karen Sanchez

Postdoctoral Research Fellow
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
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
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

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

Muhammad Esmat

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

Yasser Jaber

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

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting 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)

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B9

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)

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B2 B3 A0215

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 ·
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

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

Aku Jaakko Alexis Kammonen

Aku Jaakko Alexis Kammonen 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 Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]
<> numerical analysis (18)
Georgios Grekas

Georgios Grekas

Hierarchical ensemble-based data assimilation and rare event tracking

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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

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. Research Interests Yang's research interests include ​Probability Theory and Stochastic Processes, Numerical Analysis.

Waleed Alhaddad

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

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 Raul Tempone is an worlwide 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
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

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
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

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

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

Gaukhar Shaimerdenova 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 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 Hoel, H., Shaimerdenova, G. and Tempone, R., 202 2 . Multi -index ensemble Kalman filtering. Journal of Computational Physics. Hoel, H

Evangelia Kalligiannaki

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

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

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

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.
<> Deep learning (7) <> Monte carlo methods (7)

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

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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

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KAUST

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)

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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 ·
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 ·
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 ·
​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

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
<> Signal processing (7)
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

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

Alaa Alameer Ahmad

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

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

Peter Schmid

​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.
<> artificial intelligence (6)
Najmeddine Dhieb

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

Yasmeen Alsaedy

M.S. Student, Computer Science
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 Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)
<> Computer Vision (6)

Abdelrahman Eldesokey

Postdoctoral Research Fellow
I am a Postdoctoral Fellow at KAUST, Saudi Arabia, specializing in Generative AI and conducting research on advanced topics in Generative and Large Language Models (LLMs). I bring over 11 years of combined academic and industrial experience in Computer Vision and Deep Learning across Sweden, Egypt, and Saudi Arabia.
Najmeddine Dhieb

Najmeddine Dhieb

Karen Sanchez

Postdoctoral Research Fellow
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

Hani A. Al Majed

M.S. Student
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
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.
<> data assimilation (6)

Data Assimilation - Part 2

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B3 R5520

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

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B3 R5520

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 ·
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 Raul Tempone is an worlwide 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
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

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
<> Monte Carlo (6)

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

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B9 R2322 H1

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

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
<> Stochastic Modeling (6)

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

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B9 R2322

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 ·
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

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

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
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

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
<> bayesian inference (4)

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

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B1 R4214

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 ·
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 ·
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 Raul Tempone is an worlwide 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.
<> Computational finance (4)

Soumaya Elkantassi

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 Raul Tempone is an worlwide 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

Gaukhar Shaimerdenova 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 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 Hoel, H., Shaimerdenova, G. and Tempone, R., 202 2 . Multi -index ensemble Kalman filtering. Journal of Computational Physics. Hoel, H

Alessandro Iania

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.
<> Mathematical modeling (4)

Ali O Balubaid

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

Waleed Alhaddad

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

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

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.
<> MATLAB (4) <> numerical methods (4)

Efficient Numerical Methods for High-Dimensional Approximation Problems

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B4 L5 R5220

I​n 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

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
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

Aku Jaakko Alexis Kammonen 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 Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]
<> optimization (4)
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Chaabane Mankai

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
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

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B9 L2 H1

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.
<> Partial Differential Equations (4)

Katerina Nik

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)

B1 R4214

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) ​

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B1 R4214

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

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 differential equations (4)

Adaptive Multi Level Monte Carlo (MLMC)

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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)

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B1 R4214

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

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B3 R5209

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 Raul Tempone is an worlwide 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.
<> AI (3) <> data analysis (3)

Nizar Ajeeb

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

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

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B9 R2322

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

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
<> haps (3)

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
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
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
H

Hussein Al-Shatri

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.
<> IoT (3) <> Multilevel Monte Carlo (3)

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

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KAUST

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)

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B1 R4214

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 ·
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.
<> Photonics and optoelectronics (3) <> Quantitative finance (3)

Christian Bayer

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 ·
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

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
<> Reinforcement Learning (3) <> scientific computing (3)
Georgios Grekas

Georgios Grekas

Steven Dufour

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
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
<> statistical learning (3)
Stochastic Numerics and Statistical Learning Workshop

Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2025

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Auditorium 0215 between B2 & B3

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)

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B9

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)

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B2 B3 A0215

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.
<> stochastic algorithms (3)

Christian Bayer

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)

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B9

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)

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B2 B3 A0215

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.
<> stochastic optimization (3)

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

1 min read ·
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 ·
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 Raul Tempone is an worlwide 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.
<> Wireless communication (3) <> Wireless Communications (3) <> AI4Science (2) <> algorithms (2) <> applied mathematics (2) <> Computational biology (2)

Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

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KAUST

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

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
<> computational statistics (2) <> Generative AI and LLMs (2) <> healthcare (2) <> ISAC (2) <> Kalman filter (2) <> Laplace approximation (2) <> Large Language Models (2) <> LiDAR (2) <> Localization (2) <> markov chains (2) <> Network Design (2) <> Non-Terrestrial Networks (2) <> Numerical simulation and analysis (2)

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

1 min read ·
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

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
<> optical computing (2) <> option pricing (2)

Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

-

KAUST

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 ·
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
<> probability theory (2) <> Programming language (2) <> Python (2) <> quantum communication (2) <> spatio-temporal statistics (2) <> statistics (2) <> Stochastic Methods and Algorithms (2)

Post-Doc André Carlon participation at MCQMC2022 in Linz

1 min read ·
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 ·
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
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