Stochastic Methods and Algorithms

Post-Doc André Carlon participation at MCQMC2022 in Linz

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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

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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