About:

Inverse problems and imaging applications are everywhere, and often there is a need for assessing the uncertainty of the reconstructions due to noise, model errors, etc. by means of uncertainty quantification (UQ). Development of UQ methods requires collaboration across several areas.

This workshop brought together specialists in UQ for inverse problems and imaging, and facilitated talks related to the development of theory, methodology, and software.  We also facilitated talks about interesting applications of UQ in imaging. The goal was to stimulate networking and collaboration between researchers and students in these areas, and to present state-of-the-art research results.

The workshop was supported by the project CUQI (Computational Uncertainty Quantification for Inverse problems) funded by the Villum Foundation (grant no. 25893) and by UKRI EPSRC BLOOM (EP/V006134/1) and LEXCI (EP/W007673/1).

Plenary Speakers 

• Yoann Altmann, Heriot-Watt University
• Tatiana Bubba, University of Bath
• Per Christian Hansen, Technical Univ. of Denmark
• Aku Seppänen, University of Eastern Finland
• Julian Tachella , CNRS and ENS de Lyon
• Faouzi Triki, Grenoble-Alpes University

​CUQIpy Software Tranining Course

CUQIpy is a python software package for computational uncertainty quantification for inverse problems, developed in the CUQI research project.

Before the main workshop, a training course on this software was provided. Participants learned to use CUQIpy to model statistical inverse problems and perform UQ on them. The course included hands-on tutorials (bring your laptop!) with examples from image deblurring, X-ray CT, and inverse problems based on partial differential equations. Half of the course was devoted to working on a small use-case with CUQIpy, and participants were encouraged to bring their own case and data.

​Programme

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