Probabilistic MultiFidelity Climate Model Parameterization for Better Generalization & Extrapolation

Mohamed Aziz Bhouri: Mohamed Aziz Bhouri’s work focuses on Bayesian inference, machine learning methods for dynamical systems and model order reduction techniques. He obtained my PhD and Master of Science in Mechanical engineering and Computational Science from MIT under the supervision of Professor Anthony T. Patera and Dr. Tian Tian respectively. He obtained my bachelor degrees from Ecole Centrale Paris and University of Paris-Sud with a triple-major in Mathematics, Physics and Mechanical Engineering. He is currently working on designing multi-fidelity climate parameterization schemes for data extrapolation to warmer climates, and developing Bayesian Machine Learning methods for simultaneous parameter inference and model closure of dynamical systems. He is generally interested in computational tools and machine learning techniques for climate modeling.