Stochastic Modeling

Bayesian experimental designs by Prof. Marco Scavino (visiting Faculty at KAUST)

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B9 R2322

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.

KAUST Ph.D. student wins Society for Industrial and Applied Mathematics award

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Chiheb Ben Hammouda, a KAUST Ph.D. student in the University's Stochastic Numerics Research Group, recently won the best poster award at the Society for Industrial and Applied Mathematics (SIAM) Conference on Financial Mathematics & Engineering (FM19) held at the University of Toronto from June 4 to 7. His winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," is one of several research projects carried out by Ben Hammouda under the supervision of KAUST Professor Raul Tempone.

Sana Mazahir

Education and Early Career Sana Mazahir received the B.E. and M.S. degrees in Electrical Engineering from the College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), in 2013 and 2015, respectively. She worked at the System Analysis and Verification (SAVe) laboratory at NUST between 2015 and 2019. Currently, she is pursuing the Ph.D. degree at King Abdullah University of Science and Technology, Saudi Arabia, under the supervision of Prof. Mohamed-Slim Alouini. Her research interests include signal processing for wireless communications. Research

Marco Scavino

Marco Scavino is a Former Visiting Associate Professor​​ from Universidad de la República, Uruguay, at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Marco's research interests include Bayesian techniques for uncertainty quantification, statistical methods of experimental design, statistical procedures for model calibration, selection and validation, multivariate data analysis, statistical inference for stochastic processes, and stochastic modeling. Selected Publications A Laplace method for under

Marco Ballesio

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

Fabián Crocce

Postdoctoral Research Fellow, Stochastic Numerics Research Group
Fabián Crocce worked as a Postdoctoral Fellow​​ at Professor Raul F. Tempone's Stochastic Numerics Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Partial Differential Equations, Probability Theory, and Stochastic Processes Distributed Strategic Learning, Stochastic Modeling. Optimal stopping for diffusions, both one-dimensional and multi-dimensional. Representation of the solution by the Green kernel of the process. His research interests also included optimal stopping problems for jump-processes and infinite-horizon problems. Fourier methods

Chiheb Ben Hammouda

Chiheb Ben Hammouda 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). Prior to joining Ph.D., Chiheb obtained a Master's degree in Applied Mathematics and Computational Sciences from KAUST, and a Bachelor's degree in multidisciplinary engineering from Ecole Polytechnique de Tunisie, Tunisia. Research Interests Chiheb's research interests include Stochastic modeling, Numerical simulation and analysis, Quantitative finance in particular option pricing, Stochastic