Date: Wed. March 20, 2024
Event: FAU MoD Lecture
Organized by: FAU MoD, the FAU Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)
FAU MoD Lecture: Image Reconstruction – The Dialectic of Modelling and Learning
Speaker: Prof. Dr. Martin Burger
Affiliation: DESY, German Electron Synchrotron and Universität Hamburg
Abstract. In this talk we will discuss some current and future challenges in high-dimensional image reconstruction, which is based on the solution of large-scale inverse problems involving various uncertainties. While classical methods were purely based on physical models for forward operators and regularizations, modern machine learning techniques create the antithesis of data-driven approaches. We will discuss some pitfalls that machine learning can encounter in inverse problems and discuss opportunities for the synthesis of model- and data-driven approaches.
BIO.- Martin Burger is a leading scientist at DESY and Professor of Mathematics at the University of Hamburg, where he heads the Computational Imaging working group. Together with his team, he develops imaging reconstruction methods and novel mathematical tools with the aim of improving the reconstruction and analysis of scientific images with special concern in the mathematical foundations of machine learning and of algorithms in data science. He is a world-leading expert in inverse problems and applied mathematics and has received numerous awards and was invited speaker at all major international conferences. Martin Burger has a background in applied mathematics, specialising in inverse problems, mathematical modelling and partial differential equations.
Wed. March 20, 2024 at 14:30H (Berlin time)
On-site / Online[On-site] Friedrich-Alexander-Universität Erlangen-Nürnberg.
Room H13 Johann-Radon-Hörsaal
Cauerstraße 11, 91058 Erlangen
GPS-Koord. Raum: 49.573764N, 11.030028E [Online] FAU Zoom link
Meeting ID: 624 1094 3213 | PIN code: 694096
*This is a hybrid event (On-site / online), open to the public specially students, researchers, academics and scientific community all around the world.
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