An inverse problem refers to a situation where the quantity of interest cannot be measured directly, but only through an action of a nontrivial operator of which it is a parameter. The corresponding operator, also called forward operator, stems from a physical application modelling. Prominent examples include: Radon and Fourier transforms for X-ray CT and MRI, respectively or partial differential equations e.g. EIT or DOT. The prevalent characteristics of inverse problems is their ill-posedness i.e. lack of uniqueness and/or stability of the solution. This situation is aggravated by the physical limitations of the measurement acquisition such as noise or incompleteness of the measurements. Inverse problems are ubiquitous in applications from bio-medical, science and engineering to security screening and industrial process monitoring. The challenges span from the analysis to efficient numerical solution. This conference will bring together mathematicians and statisticians, working on theoretical and numerical aspects of inverse problems, as well as engineers, physicists and other scientists, working on challenging inverse problem applications. We welcome industrial representatives, doctoral students, early career and established academics working in this field to attend. Topic list: • Imaging • Inverse problems in partial differential equations (Memorial Lecture for Slava Kurylev) • Model and data driven methods for inverse problems • Optimization and statistical learning • Statistical inverse problems Invited Speakers Julie Delon (MAP5, Paris Descartes University) Markus Haltmeier (University of Innsbruck) Mike Hobson (University of Cambridge) Matti Lassas (University of Helsinki) Gabriel Peyre (DMA, École Normale Supérieure) Michael Unser (EPFL)