M8. Inverse Problems with Data-Driven Methods and Deep Learning

In recent years, the volume and complexity of various data obtained by modern applications has been steadily increasing. Extracting consistent scientific information from these large datasets remains an open challenge for the community, and data-driven approaches such as deep learning have quickly emerged as a potentially powerful solution to some long-term problems. In this context, robust mathematical inversion algorithms, combined with new data science techniques, deliver state-of-the-art results across a wide range of inverse problems.

This mini-symposium aims at bringing together experts in data-driven methods and deep learning for inverse problems and provides an overview of learned image reconstruction approaches, mathematical insights, and real-world applications.

Organizers:
Tatiana Bubba, University of Bath, UK, This email address is being protected from spambots. You need JavaScript enabled to view it. 
Andreas Hauptmann, University of Oulu, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it. 
Luca Ratti, University of Bologna, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.

Invited Speakers:
Babak Maboudi Afkham, Technical University of Denmark, Denmark, This email address is being protected from spambots. You need JavaScript enabled to view it.
Goal-oriented uncertainty quantification for inverse problems via Variational Encoder-Decoder Networks

Vegard Antun, University of Oslo, Norway, This email address is being protected from spambots. You need JavaScript enabled to view it.
Implicit regularization in AI meets generalized hardness of approximation: Sharp results for diagonal linear networks

Davide Bianchi, Harbin Institute of Technology, China, This email address is being protected from spambots. You need JavaScript enabled to view it.

Marcello Carioni, University of Twente, The Netherlands, This email address is being protected from spambots. You need JavaScript enabled to view it.
Optimal transport methods for inverse problems regularization

Neil K. Chada, Heriot Watt University, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
On a dynamic variant of the iteratively regularized Gauss-Newton Method with sequential data

Margaret Duff, University of Bath, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.
Regularisation using VAEs with structured image covariance

Felix Herrmann, Georgia Institute of Technology, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.
Neural wave-based imaging with amortized uncertainty quantification

Johannes Hertrich, Technische Universität Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Generative sliced MMD flows for posterior sampling in Bayesian Inverse

Erich Kobler, University of Bonn, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Learning Gradually Non-convex Image Priors Using Score Matching

Andreas Kofler, Physikalisch-Technische Bundesanstalt (PTB), Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Deep learning for image reconstruction: robustness, model-capacity and accuracy

Rémi Laumont, Technical University of Denmark, Denmark, This email address is being protected from spambots. You need JavaScript enabled to view it.
Bayesian computation with Plug-and-Play (PnP) priors for inverse problems in imaging sciences

Dong Liu, University of Science and Technology of China, China, This email address is being protected from spambots. You need JavaScript enabled to view it.
Unsupervised neural networks for image Reconstruction

Subhadip Mukherjee, IIT Kharagpur, India, This email address is being protected from spambots. You need JavaScript enabled to view it.
Provably convergent plug-and-play quasi-Newton methods

Sebastian Neumayer, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, This email address is being protected from spambots. You need JavaScript enabled to view it.

Thomas Pinetz, Bonn University, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Shared prior learning for general inverse problems

Robert Scheichl, University of Heidelberg, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.
Deep Inverse Rosenblatt Transport for Bayesian Inverse Problems

Tan Bui-Thanh, University of Texas, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.