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.
https://sri-uq.kaust.edu.sa/Pages/JoakimBeck.aspx