M29: Inverse Problems with Data-Driven Methods and Deep Learning

We are currently experiencing a paradigm shift in image reconstruction. Robust mathematical inversion algorithms are combined with emerging methods in data science to achieve state-of-the-art results in a wide range of inverse problems. A successful application of these methods in practice involves a thorough understanding of their mathematical properties and the underlying imaging physics. This minisymposium aims to bring experts in data-driven methods and deep learning for inverse problems together and provides an overview of learned image reconstruction approaches, mathematical insights, and real-world applications.

Organizers:
Martin Genzel, Utrecht University, Netherlands, 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.
Maximilian März, Technical University of Berlin, Germany, This email address is being protected from spambots. You need JavaScript enabled to view it.

Speakers (will be anounced later):

More in this category: « Poster Session

PLENARY SPEAKERS

Prof. Dr. Liliana Borcea

University of Michigan, USA
http://www-personal.umich.edu/~borcea/

Inverse scattering in random media, Electro-magnetic inverse problems, Effective properties of composite materials, transport in high contrast, heterogeneous media

Prof. Dr. Bernd Hofmann

Chemnitz University of Technology, Germany
https://www.tu-chemnitz.de/mathematik/inverse_probleme/hofmann/?en=1

Regularization of inverse and ill-posed problems

Prof. Dr. John C Schotland

Yale University, USA
https://gauss.math.yale.edu/~js4228/

Inverse problems with applications to imaging, Scattering theory, Waves in random media, Nano-scale optics, Coherence theory and quantum optics

Prof. Dr. Erkki Somersalo

Case Western Reserve University, USA
https://mathstats.case.edu/faculty/erkki-somersalo/

Computational and statistical inverse problems, Probabilistic methods for uncertainty quantification, Modeling of complex systems, Biomedical applications

Prof. Dr. Gunther Uhlmann

University of Washington, USA
https://sites.math.washington.edu/~gunther/

Inverse problems and imaging, Partial differential equations, Microlocal analysis, Scattering theory

Prof. Dr. Jun Zou

The Chinese University of Hong Kong, Hong Kong SAR
https://www.math.cuhk.edu.hk/~zou/

Numerical solutions of electromagnetic Maxwell systems and interface problems, inverse and ill-posed problems, preconditioned and domain decomposition methods