Joakim Beck has joined as a Post Doctoral Fellow the Stochastic Numerics Group and SRI Uncertainty Quantification Center at KAUST.
Joakim is an applied mathematician specializing in computational science and uncertainty quantification.

He obtained a M.Sc. degree in Engineering Physics at the Royal Institute of Technology [KTH] in Stockholm, Sweden, and a Ph.D. in Chemical Engineering at University College London (UCL), UK. His Ph.D. research was focused on using Gaussian Process regression for optimization with application to the design of a carbon capture technology in power plants. He has also been working on Stochastic Collocation techniques for partial differential equations with random coefficients.

From 2013-2016, he held a Post Doctoral Research Associate position at UCL’s Department of Statistical Science and the Institute of Risk and Disaster Reduction to develop a new method for design of computer experiments. The aim was to develop a computationally efficient method that can be employed to learn about parameter uncertainties in tsunami modeling.

His research interests are in the development of computationally efficient methods for uncertainty quantification. More specifically, his focus is on the development of algorithms to numerically solve various ordinary and partial differential equations with random input data.

The position at KAUST is joint with EPFL, and the core of his work is on developing and applying new multi level / multi index methods for forward and inverse problems in uncertainty quantification.