bayesian inference

Amin Wu

Amin Wu successfully defended her PhD Thesis

1 min read · Tue, Mar 3 2026

bayesian inference stochastic processes stochastic differential equations multilevel methods

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

Bayesian Inference for Partially Observed Continuous-Time Processes

Amin Wu, Ph.D. Student, Statistics

Mar 3, 10:00 - 12:00

B5 L5 R5220

McKean-Vlasov SDEs bayesian inference markov chains Monte Carlo

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

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

Prof. Youssef Marzouk, Massachusetts Institute of Technology, USA

Apr 22, 14:00 - 15:00

B1 R4214

bayesian inference sequential Monte Carlo

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

Post-Doc André Carlon participation at MCQMC2022 in Linz

1 min read · Mon, Aug 1 2022

bayesian inference Stochastic Methods and Algorithms

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

Post-Doc André Carlon participation at UNCECOMP2023 in Athens

1 min read · Sun, Jul 30 2023

bayesian inference Stochastic Methods and Algorithms

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

Raul Tempone

Professor, Applied Mathematics and Computational Science

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

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