André Gustavo Carlon

June 2026

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  Article "Multi-Iteration Stochastic Optimizers" published in Applied Mathematics & Optimization

  2 min read · Sun, Jun 14 2026

  News

  The article “ Multi-Iteration Stochastic Optimizers” by André Carlon, Luis Espath, Rafael Holdorf, and Raúl Tempone has been published in Applied Mathematics & Optimization. The paper introduces a new class of first-order stochastic optimization methods based on the Multi-Iteration stochastiC Estimator (MICE). The central idea is to reuse gradient information collected along the optimization path through successive control variates. By exploiting correlations between nearby iterates, MICE reduces the variance of stochastic gradient estimates while keeping the additional sampling cost under

April 2024

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  New Publication in Optimization Methods and Software by Our Research Team

  1 min read · Wed, Apr 3 2024

  News

  We are proud to announce a significant achievement by our research team: the acceptance of our paper, "Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization," in the prestigious journal Optimization Methods and Software. This work is a collaborative effort by André Carlon, a postdoctoral fellow at the Stochastic Numerics group at KAUST, Raúl Tempone, the PI of the group, and Luis Espath, a professor at the University of Nottingham. Our research presents a novel approach to improving the performance of stochastic optimization

December 2023

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  Research visit to RWTH, Aachen by Andre Carlon

  1 min read · Sun, Dec 10 2023

  News

  Between November 21st and December 2nd, André Carlon, one of the postdocs in our group, visited the Chair of Uncertainty Quantification (UQ) at RWTH University in Aachen, Germany. His visit was marked by fruitful collaborations on various UQ-related projects. In the photo, from left to right, are Dr. Alexander Litvinenko, Group Leader in UQ, Prof. Abdul-Lateef Haji-Ali, Associate Professor at Heriot-Watt University, and André Carlon

July 2023

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  Post-Doc André Carlon participation at UNCECOMP2023 in Athens

  1 min read · Sun, Jul 30 2023

  News

  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

December 2022

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  Academic collaboration between KAUST and RWTH Aachen in a new project

  1 min read · Thu, Dec 1 2022

  News

  Between November 22nd and December 1st, 2022, the StochNum Research Group hosted Truong-Vinh Hoang, a postdoctoral fellow at the Alexander von Humboldt Mathematics for Uncertainty Quantification Chair in RWTH-Aachen. During his visit, Hoang worked with André Gustavo Carlon on a new project about the robustness certification of machine learning models, with the collaboration of Prof. Bernard Ghanem from the Visual Computer Center at the AI Initiative at KAUST. The main goal of this collaborative project is to develop numerical methods to improve the robustness of machine learning models to

August 2022

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

  1 min read · Sun, Aug 28 2022

  News

  stochastic optimization Bayesian Estimation machine learning

  A preprint of a new research project of our group named " Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization" is available at arXiv. The manuscript is authored by André Carlon, Prof. Luis Espath, and Prof. Raúl Tempone. Abstract: Using quasi-Newton methods in stochastic optimization is not a trivial task. In deterministic optimization, these methods are often a common choice due to their excellent performance regardless of the problem's condition number. However, standard quasi-Newton methods fail to extract curvature
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  Post-Doc André Carlon participation at MCQMC2022 in Linz

  1 min read · Mon, Aug 1 2022

  News

  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