Bayesian Inverse Problems: Methods, Tools, and Applications
This talk will discuss methods, tools and applications for solving inverse problems.
Overview
Solving inverse problems involves inferring the unknown parameters of a mathematical model of a physical or conceptual system from the measured data of its response. Such problems are prevalent in many application areas, including geoscience, engineering, and medicine. They are often challenging due to ill-posedness, measurement noise, model complexity, and computational cost. Additionally, multiple parameter configurations may fit the observed data equally well.
Presenters
Brief Biography
Amal Alghamdi is a computational scientist and founder of Impact Alpha, Saudi Arabia. She holds a Ph.D. and M.S. in Computational Science, Engineering, and Mathematics from the University of Texas at Austin, and another M.S. in Computer Science from King Abdullah University of Science and Technology (KAUST). Before founding Impact Alpha, she was a postdoctoral researcher in the Scientific Computing section at the Technical University of Denmark.
