About Marco Ballesio Marco Ballesio Ph.D., Applied Mathematics and Computational Science Stochastic Modeling uncertainty quantification 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 Articles Related News December 2019 Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty 1 min read · Sun, Dec 8 2019 News Seismic Wave Propagation Multilevel Monte Carlo 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. March 2016 Visiting student Marco Ballesio successfully defended his Master Thesis with grade 110/110 CUM LAUDE. Politecnico of Torino and Real Collegio Carlo Alberto. Italy - March 2016 1 min read · Sun, Mar 20 2016 News Marco Ballesio successfully defended his Master Thesis "Indirect Inference for Scalar Time-homogeneous Stochastic Differential Equations Based on Moment Expansions" at Politecnico of Torino and Real Collegio Carlo Alberto. He wrote his Thesis at KAUST in the period July 2015 - January 2016 oriented by Raul Tempone, and Pedro Vilanova (Stochastic Numerics Research Group).
Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty 1 min read · Sun, Dec 8 2019 News Seismic Wave Propagation Multilevel Monte Carlo 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.
Visiting student Marco Ballesio successfully defended his Master Thesis with grade 110/110 CUM LAUDE. Politecnico of Torino and Real Collegio Carlo Alberto. Italy - March 2016 1 min read · Sun, Mar 20 2016 News Marco Ballesio successfully defended his Master Thesis "Indirect Inference for Scalar Time-homogeneous Stochastic Differential Equations Based on Moment Expansions" at Politecnico of Torino and Real Collegio Carlo Alberto. He wrote his Thesis at KAUST in the period July 2015 - January 2016 oriented by Raul Tempone, and Pedro Vilanova (Stochastic Numerics Research Group).
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