Inverse problems involve the recovery of unknown model parameters from noisy indirect observations. They are central to myriad applications, e.g., in imaging, geophysics, and remote sensing. Current research involves understanding computational and statistical aspects of inference with complex models, particularly partial differential equations, and the development of new data-driven methodologies for formulating and solving inverse problems given rich sources of information.
This workshop will survey recent theoretical and algorithmic advances in the field, collectively illustrating an interplay between optimization, sampling, machine learning, and statistical theory. The workshop has natural connections to other FoCM 2026 workshops on Computational Optimal Transport, Foundations of Data Science and Machine Learning, Foundations of Numerical PDEs, and Continuous Optimization, among others.
Organizers
MIT
University of Chicago
University of Cambridge
Speakers
Semi-plenary speakers
University of Texas at Austin
University of Klagenfurt
Invited speakers
DESY & University of Hamburg
University of Utah
FU Berlin
Emory University
University of Cambridge
Bocconi University