Approximative Bayesian Optimal Experimental Design in Inverse Problems
- Prof. Tapio Helin, LUT University, Finland
Overview
Abstract:
Data collection, whether in field experiments or in the laboratory, is often restricted by limited resources. It can be difficult, expensive and time-consuming, which puts severe limits on the quality of data acquired. Bayesian inference provides a principled approach to quantify and optimise the information gained from any given experimental setup. However, the required computational effort in large-scale inverse problems still remains prohibitive. In this talk I discuss stability of Bayesian optimal experimental design (OED) under perturbation such as surrogate models. Moreover, I overview some recent work on Bayesian OED in imaging.
Presenters
Prof. Tapio Helin, LUT University, Finland
Brief Biography
Tapio Helin is a Professor of Applied Mathematics at LUT University in Finland. He received his DSc from Aalto University in 2010, and worked as a research scientist at the Johann Radon Institute (RICAM) in Linz, Austria, and as a researcher at the University of Helsinki. From 2018 to 2023 he was an Academy Research Fellow, and in 2020 he was a Visiting Associate at Caltech. He joined LUT in 2019 and became a full professor in 2022.
His research covers inverse problems, uncertainty quantification, and statistical learning. In particular, he has focused on optimal experimental design. He is a principal investigator in the Finnish Flagship of Advanced Mathematics for Sensing, Imaging and Modelling (FAME), and was previously a PI in the Centre of Excellence in Inverse Modelling and Imaging. He is also Vice-President of the Finnish Inverse Problems Society.