Events

Nov 28 - Nov 22, 2026

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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2026

  Nov 22, 08:30 - Dec 2, 13:30

  B2/3 A0215

  Workshops

  stochastic algorithm numerical analysis statistical learning

  Bringing together experts in stochastic algorithms, numerical analysis and statistical learning with applications in climate modeling and quantitative finance.

May 23 - May 17, 2026

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  Unlocking Simulators: From Screening to Fingerprints via Sensitivity Analysis

  Nabila Bounceur, Research Scientist, Stochastic Numerics Research Group

  May 19, 13:00 - 14:00

  Zoom

  probabilistic sensitivity analysis

  Abstract Simulators are computer codes nowadays ubiquitous to the analysis of complex physical systems. The premise of this work is to view a simulator as a black-box, mathematically represented by a function that one wishes to analyze only on the basis of information on its inputs and outputs. Performing experiments by running a simulator at specific input configurations and/or experimental settings helps infer insights about underlying processes and behaviors of the complex physical system it represents. In this context, the concept of sensitivity analysis (SA) is central. Broadly speaking

May 16 - May 10, 2026

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  When Samples Correlate Symmetrically: Particle Dynamics for Optimal Transport

  Prof. Hossein Gorji

  May 12, 13:00 - 14:00

  Zoom

  Abstract In this talk, I will introduce orthogonal coupling dynamics (OCD), a dynamical approach to address the Monge–Kantorovich problem. The main idea is to evolve a coupling between two fixed marginals by projecting the gradient descent of the transport cost onto a marginal-preserving tangent subspace. The resulting dynamics is expressed through conditional expectations: it keeps each marginal unchanged while monotonically reducing the expected transport cost. For the quadratic cost, OCD admits a simple particle interpretation in which samples interact through regression-like conditional

May 2 - Apr 26, 2026

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  Quasi-Monte Carlo with A Hankel Random Digital Net

  Yang Liu , Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Apr 28, 13:00 - 14:00

  Zoom

  Abstract This paper proposes a new randomized design of digital nets in which the generating matrices are chosen to be random Hankel matrices. Compared with previous randomized design of digital nets, this approach simplifies the construction process and reduces the number of random variables required, while still achieving desirable convergence rates when combined with appropriate estimators. We analyze the properties of the proposed design, derive bounds for Walsh coefficients, and provide error analysis for both the median-of-means estimator and a newly proposed greedy selection estimator

Apr 11 - Apr 5, 2026

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  Dynamical Low-Rank Approximations for Kalman Filtering

  Dr. Thomas Trigo Trindade, King Abdullah University of Science and Technology (KAUST)

  Apr 7, 13:00 - 14:00

  Zoom

  Abstract Data Assimilation consists in combining model knowledge with a stream of observations to improve the prediction of a system's state. Two prominent instances of this approach are the Kalman-Bucy filter (KBF) and its particle-based analog, the Ensemble Kalman filter (ENKF). While the former describes the exact filtering density evolution under linear-Gaussian dynamics, the latter is widely used in real-world applications such as climate and geosciences due to its computational tractability. However, using a small ensemble may lead to significant Monte Carlo error and stochastic

Apr 4 - Mar 29, 2026

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  Deep Learning Methods for Backward Stochastic Volterra Integral Equations with Applications

  Prof. Nacira Agram, Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden

  Mar 31, 13:00 - 14:00

  Zoom

  Deep learning Stochastic Control

  Abstract Backward stochastic Volterra integral equations (BSVIEs) provide a natural framework for modeling stochastic systems with memory and path dependence. Such equations arise in several areas including recursive utilities, stochastic control, rough volatility models, and path-dependent partial differential equations. Despite their importance, numerical methods for BSVIEs remain relatively limited in the literature due to the presence of bi-temporal processes and the resulting analytical and computational challenges. In this talk, I present a deep learning framework for approximating the

Mar 14 - Mar 8, 2026

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  Dynamic measure transport for sampling and quantization

  Youssef Marzouk, Professor, MIT

  Mar 10, 18:00 - 19:00

  Zoom

  Sampling or otherwise summarizing complex probability distributions is a central task in applied mathematics, statistics, and machine learning. Many modern algorithms for this task introduce dynamics in the space of probability measures, designing these dynamics to achieve good practical performance. We will discuss several aspects of this broad design endeavor. First is the problem of optimal scheduling of dynamic transport, i.e., with what speed should one proceed along a prescribed path of probability measures? Though many popular methods seek straight line trajectories, i.e., trajectories

Mar 7 - Mar 1, 2026

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  When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis

  Xiang Li, Ph.D. student, Department of Computer Science, ETH Zurich

  Mar 3, 14:00 - 15:00

  Zoom

  Abstract Score-based methods, such as diffusion models and Bayesian inverse problems, are often interpreted as learning the data distribution in the low-noise limit ($\sigma \to 0$). In this work, we propose an alternative perspective: their success arises from implicitly learning the data manifold rather than the full distribution. Our claim is based on a novel analysis of scores in the small-$\sigma$ regime that reveals a sharp separation of scales: information about the data manifold is $\Theta(\sigma^{-2})$ stronger than information about the distribution. We argue that this insight
• 

  Bayesian Inference for Partially Observed Continuous-Time Processes

  Amin Wu, Ph.D., 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.

Feb 21 - Feb 15, 2026

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  A Pontryagin Maximum Principle on the Belief Space for Continuous-Time Stochastic Optimal Control with Discrete Observations

  Saifeddine Ben Naamia, Ph.D. student, Department of Mathematics, RWTH Aachen University

  Feb 17, 14:00 - 15:00

  Zoom

  Partially Observed Stochastic Optimal Control

  Abstract This talk presents joint work with Christian Bayer, Saifeddine Ben Naamia, Erik von Schwerin, and Raúl Tempone on continuous-time stochastic optimal control under partial observations available only at discrete time instants. We formulate the problem on the space of beliefs, using the controller’s posterior distribution as the state variable, and derive a Pontryagin maximum principle that accounts for continuous evolution between observations and Bayesian jump updates at observation times. A key result links the adjoint process to the gradient of the value functional on the belief

Feb 14 - Feb 8, 2026

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  Robust Anomaly Detection under Severe Class Imbalance: An Adaptive Geometric Sampling Approach

  Kyoung-Sook Moon, Professor, Department of Finance & Big Data, Gachon University, Gyeonggi, Republic of Korea

  Feb 10, 14:00 - 15:00

  Zoom

  Abstract Detecting rare but high-impact anomalies in financial data remains a fundamental challenge due to severe class imbalance and overlapping class distributions. Conventional resampling techniques such as SMOTE often improve recall at the expense of precision by generating ambiguous synthetic samples near decision boundaries. In this talk, I present an adaptive sampling framework that integrates geometry-aware oversampling with ensemble-based undersampling to address these limitations. The proposed geometric oversampling method generates synthetic minority samples within localized regions

May 24 - May 18, 2025

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  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.

Mar 29 - Mar 23, 2025

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  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.

Dec 7 - Dec 1, 2024

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  Multilevel sampling and goal-oriented adaptive finite elements for random PDEs and advances in randomized quasi-Monte Carlo

  Yang Liu , Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Dec 4, 16:00 - 18:30

  B2, L5, R5209

  This thesis explores numerical methods for uncertainty quantification in systems governed by random partial differential equations (PDEs) and advances in randomized quasi-Monte Carlo (RQMC) techniques.

May 25 - May 19, 2024

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  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.

May 18 - May 12, 2024

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  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

  numerical analysis

  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.

Mar 23 - Mar 17, 2024

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  Tracking rare events within the ensemble Kalman filtering

  Gaukhar Shaimerdenova, Research Consultant, Stochastic Numerics Research Group

  Mar 21, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF).

Mar 16 - Mar 10, 2024

• 

  Randomized quasi-Monte Carlo and Owen's boundary growth conditions: A spectral analysis

  Yang Liu , Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Mar 14, 12:00 - 13:00

  B9 L2 H2 H2

  In this work, we analyze the convergence rate of randomized quasi-Monte Carlo (RQMC) methods under Owen's boundary growth condition [Owen, 2006] via spectral analysis.

May 27 - May 21, 2023

• 

  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.

May 21 - May 15, 2022

• 

  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop

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

  May 15, 08:00 - May 28, 17:00

  KAUST Campus

  This scientific meeting will concentrate on stochastic algorithms and their rigorous numerical analysis for various problems, including statistical learning, optimization, and approximation. Stochastic algorithms are valuable tools when addressing challenging computational problems.

Jan 15 - Jan 9, 2022

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  Multilevel Methods for Stochastic Forward and Inverse Problems

  Marco Ballesio, Ph.D., Applied Mathematics and Computational Science

  Jan 12, 11:00 - 12:00

  KAUST

  This thesis studies novel and efficient computational sampling methods for applications in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation.

Jul 4 - Jun 28, 2020

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  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.

May 4 - Apr 28, 2019

• 

  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.

Feb 9 - Feb 3, 2019

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  Causality and Bayesian network PDE for multiscale representations of porous media by Dr. Eric Hall

  Dr. Eric Hall

  Feb 6, 14:00 - 15:00

  B1 L4 R4214

  Hierarchical porous media that feature properties and processes at multiple scales arise in many engineering applications including the design of novel materials for energy storage devices. Microscopic (pore-scale) properties of the media impact their macroscopic (continuum- or Darcy-scale) counterparts and understanding the relationships between processes on these two scales is essential for informing engineering decision tasks.
• 

  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

  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.

Oct 13 - Oct 7, 2018

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  Rare Events Simulations with Applications to the Performance Evaluation of Wireless Communication Systems by Nadhir Ben Rached, PhD Student of Prof. Slim Alouini, KAUST

  Nadhir Ben Rached, Ph.D., Applied Mathematics and Computational Science

  Oct 8, 09:00 - 10:30

  B1 R3119

  The probability that a sum of random variables (RVs) exceeds (respectively falls below) a given threshold, is often encountered in the performance analysis of wireless communication systems. Generally, a closed-form expression of the sum distribution does not exist and a naive Monte Carlo (MC) simulation is computationally expensive when dealing with rare events. An alternative approach is represented by the use of variance reduction techniques, known for their efficiency in requiring less computations for achieving the same accuracy requirement.

Sep 29 - Sep 23, 2018

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  Statistical analysis and Bayesian methods for fatigue life prediction and inverse problems in linear time dependent PDEs with uncertainties by Zaid Sawlan

  Zaid Sawlan, Postdoctoral Research Fellow, Stochastic Numerics Research Group

  Sep 26, 16:00 - 17:30

  B3 R5209

  This work employs statistical and Bayesian techniques to analyze mathematical forward models with several sources of uncertainty. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data. One of the critical challenges in real-world applications is, for any proposed model, to estimate and quantify uncertainties of its unknown parameters using the available observations.

Jul 21 - Jul 15, 2018

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  Stochastic Model Hyperreduction for Modeling and Quantifying Model-Form Uncertainties by Professor Charbel Farhat

  Professor Charbel Farhat

  Jul 15, 15:00 - 16:00

  B3 L5 R5220

  A feasible, nonparametric, probabilistic approach for modeling and quantifying model-form uncertainties associated with a High-Dimensional Computational Model (HDM) and/or a corresponding Hyperreduced Projection-based Reduced-Order Model (HPROM) [1,2] designed for the solution of computational mechanics problems is presented.

Aug 26 - Aug 20, 2017

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  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.

Apr 15 - Apr 9, 2017

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  Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models by Soumaya ElKantassi

  Soumaya Elkantassi, M.S., Applied Mathematics and Computational Science

  Apr 12, 14:30 - 16:00

  B3 L5 R5209

  Ms. Soumaya ElKantassi is a master student in Applied Mathematics and Computational Science and a member of the Stochastic Numerics Group at KAUST. She obtained her Bachelor's degree in Engineering from Tunisia Polytechnic School majoring in Economic and Scientific Management.

May 28 - May 22, 2016

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  KAUST Uncertainty Quantification School on Numerical Methods for Direct and Inverse Problems

  May 22, 09:00 - May 28, 17:00

  B9 H1

  See the program here Technical presentations School topics: Multi-Level and Multi Index Sampling Bayesian Inverse Problems Markov Chain and Sequential Monte Carlo Data Assimilation and Filtering Techniques Low-Rank Approximation Organizers: ​Prof. Raul Tempone (KAUST) Prof. Fabio Nobile (EPFL) External speakers: ​​Fabio Nobile​, Associate Professor, CADMOS Chair of Scientific Computing and Uncertainty Quantification​, EPFL - Switzerland​ ​André Uschmajew, Professor​, University of Bonn​, Germany​​​​ Ajay Jasra​, Associate Professor​, National University of Singapore​, Singapore​ Nuutti Hyvönen

May 21 - May 15, 2016

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  PhD Defense: Efficient Multilevel and Multi-index Sampling Methods for Stochastic Differential Equations by Abdul Lateef Haji Ali, PhD candidate of Prof. Raul Tempone (KAUST)

  May 19, 14:00 - 15:00

  B2 R5220

  Most problems in engineering and natural sciences involve parametric equations in which the parameters are not known exactly due to measurement errors, lack of measurement data, or even intrinsic variability. In such problems, one objective is to compute point or aggregate values, called "quantities of interest".

Jan 9 - Jan 3, 2016

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  Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2016)

  Omar Knio, Dean, Computer, Electrical and Mathematical Sciences and Engineering

  Jan 5, 08:30 - Jan 10, 18:30

  B4 B5 A0215

  Jan. 5th to 10th, 2016 - 4th SRI UQ Workshop at KAUST January 5-7, 2016 - Level 0 Auditorium, between Al-Jazri and Al-Kindi (buildings 4 and 5), KAUST January 8-10, 2016 - Juman Conference Room, Bay La Sun Hotel, KAEC See please the program here Book of abstracts​ Technical presentations slides and videos The registration is now closed.​ ​​The deadline for poster submission is December 16th, 2015.

Oct 17 - Oct 11, 2015

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  Some aspects of time inconsistence in stochastic optimal control

  Professor of mathematical statistics, KTH, Sweden

  Oct 13, 14:00 - 15:00

  B9 H1

  Abstract: Time inconsistent performance criteria are natural in optimization problems in themes such as large scale cooperative multi-agent systems including the principle-multi-agent (leader-follower) problem​ and crowd dynamics, diffusion of energy or fluids in non-homogeneous media, risk management of uncertain environment such as risks in river and coastal hydrology due to climate changes etc. I will comment on some challenges faced by modern optimization techniques when the performance criteria are time-inconsistent. I will show for example that time-inconsistency makes the Dynamic

Sep 12 - Sep 6, 2015

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  Chebyshev nodes in multiple dimensions by PhD candidate Sören Wolfers (KAUST)

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

  Sep 10, 14:30 - 16:00

  B1 R4214

  Chebyshev nodes are well-known for their near-optimal polynomial interpolation and quadrature properties on intervals. In particular, the associated Lebesgue constant grows only logarithmically, whereas that associated to equispaced nodes grows exponentially.
• 

  A regularising ensemble Kalman method for PDE-constrained inverse problems By Prof. Marco Iglesias (University of Nottingham, U.K.​)

  Dr. Marco A. Iglesias, Assistant Professor in Scientific Computation, School of Mathematical Sciences, University of Nottingham

  Sep 8, 14:30 - 16:00

  B1 R4214

  We present a novel regularizing ensemble Kalman method for solving PDE-constrained inverse problems. By merging ideas from iterative regularisation approaches and ensemble Kalman algorithms we design a derivative-free solver for generic inverse problems. The proposed method can be used to estimate parameters of large-scale PDE models in a black-box fashion.

Aug 8 - Aug 2, 2015

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  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.

May 9 - May 3, 2015

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  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

  Monte carlo methods

  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.

May 2 - Apr 26, 2015

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  Drift-Implicit Multi-Level Monte Carlo Tau-Leap Methods for Stochastic Reaction Networks by Chiheb Ben Hammouda (Master Student of Prof. Raul Tempone, KAUST)

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

  Apr 30, 16:00 - 17:00

  B1 R4214

  In biochemical systems, stochastic effects can be caused by the presence of small numbers of certain reactant molecules. In this setting, discrete state-space and stochastic simulation approaches were proved to be more relevant than continuous state-space and deterministic ones.
• 

  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.
• 

  An Integrated Approach for Model-Based Systems and Software Engineering

  Prof. Jacques Duysens, Systems Business Development Director for EMEA at ANSYS, Inc

  Apr 29, 14:00 - 15:00

  B1 R4214

  Jacques DUYSENS, 47 years old, obtained his Civil Engineer Diploma in Electricity & Mechanics, and in Aerospace, from the University of Liège (Belgium) with the highest distinction. His prior responsibility was as R&D Engineer in Powertrains Groups dynamics at RENAULT SA (France) in 1990. Three years later, he served as Director of the Simulation of Manufacturing Processes department (Assembly & Machining) within the Mechanical Process Direction.

Jan 31 - Jan 25, 2015

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  Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models by Alvaro Moraes

  Alvaro Moraes, Postdoctoral Research Fellow, Stochastic Numerics Research Group

  Jan 26, 14:00 - 15:00

  Al Marsa Yacht Club

  Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs) that have been mainly developed to model biochemical reactions. SRNs also have applications in neural networks, virus kinetics, and dynamics of social networks, among others.

Jan 24 - Jan 18, 2015

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  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.
• 

  A Bayesian level-set approach for geometric inverse problems

  Prof. Marco A. Iglesias, School of Mathematical Sciences, University of Nottingham

  Jan 18, 15:00 - 16:00

  B1 R4214

  Marco Iglesias received his PhD in 2008 from the Institute for Computational Engineering and Sciences, University of Texas at Austin. From 2008 and 2013 Iglesias held postdoctoral positions at the Department of Civil and Environmental Engineering at MIT as well as the Mathematics Institute at the University of Warwick . Since October 2013 Iglesias is an assistant professor in scientific computation at the University of Nottingham, U.K.

Jan 10 - Jan 4, 2015

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  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

  uncertainty quantification algorithms

  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​

Nov 8 - Nov 2, 2014

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  On Adaptive Multilevel Monte Carlo and Multi-Index Monte Carlo by Prof. Raul Tempone

  Raul Tempone, Professor, Applied Mathematics and Computational Science

  Nov 6, 15:00 - 16:00

  B9 R2322

  We provide a quick glance into recently developed Adaptive Multilevel Monte Carlo (MLMC) Methods for the following widely applied mathematical models: (i) Itô Stochastic Differential Equations, (ii) Stochastic Reaction Networks modeled by Pure Jump Markov Processes and (iii) Partial Differential Equations with random inputs.

Oct 25 - Oct 19, 2014

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  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.

Oct 18 - Oct 12, 2014

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  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

  Monte carlo methods

  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.

Aug 30 - Aug 24, 2014

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  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.

May 10 - May 4, 2014

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  Static and Sequential Probabilistic Inverse Problems: An Extreme-Scale Challenge Application By Dr. Kody Law

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

  May 8, 12:30 - 13:30

  B9 H2

  In many Computational Science and Engineering applications, uncertainties in model, parameters, and initial condition, in concert with increasing computational power, have lead to an increasing interest in probabilistic solutions of large-scale problems. Even crude approximations of the probabilistic solution require many tens of deterministic solves and accurate ones often require thousands or millions.