Organising Committee:

Marcelo Pereyra, Heriot‐Watt University & Maxwell Institute ‐ Conference Chair

Yoann Altmann, Heriot‐Watt University

Konstantinos Zygalakis, University of Edinburgh & Maxwell Institute

Mike Davies, University of Edinburgh
Scientific Committee:

Simon Arridge, University College London

Marta Betcke, University College London

Martin Benning, Queen Mary University of London

Thomas Blumensath, University of Southampton

Tatiana Bubba, University of Bath

Julie Delon, Paris Descartes University

Matthias Ehrhardt, University of Bath

Jean‐François Giovannelli, University of Bordeaux

Sean Holman, University of Manchester

Clifford Nolan, University of Limerick

Clarice Poon, University of Bath

Audrey Repetti, Heriot‐Watt University & Maxwell Institute
About:
Inverse problems are widespread in many varied fields such as medical and satellite imaging, biology, astronomy, geophysics, environmental sciences, computer vision, energy, finance, and defence. These problems are inverse in the sense that they arise from seeking to use a mathematical or physical model “backwards” to indirectly determine a quantity of interest from the effect that this quantity causes on some observed data.
A main challenge resulting from using models “backwards” to measure causes from their effects is that solutions are often not well posed, i.e., not unique and/or unstable with respect to small perturbations in the data. This difficulty has stimulated an important amount of research and innovation at the interface of applied mathematics, statistics, engineering, physics, and other fields, leading to great social and economic benefit through impact on science, medicine, and engineering.
The aim of this conference was to bring together the applied mathematics, statistics, machine learning, engineering, physics and industrial communities around the topic of inverse problems to discuss recent developments and open challenges in theory, methodology, computational algorithms, and applications. We welcomed industrial representatives, doctoral students, early career and established academics working in this field.
Topics of interest include, for example:
 Inverse problems in mathematical and computational imaging;
 Inverse problems in science, medicine, engineering, and other fields;
 Model‐based and data‐driven methods for solving inverse problems;
 Optimisation, statistical, and machine learning methods for solving inverse problems;
 Mathematical theory for inverse problems;
 Deterministic and stochastic computational methods and algorithms.
Invited Speakers:
Thomas Pock, Graz University of Technology 
Gabriele Steidl, Berlin Institute of Technology 
Jason McEwen, University College London & Kagenova 
Yi Yu, University of Warwick 
Andrew Duncan, Imperial College London 
Luca Calatroni, CNRS & Nice University 