Compressed Sensing in Information Processing / Applied and Numerical Harmonic Analysis (PDF)

she received an Einstein Chair at TU Berlin in 2008, a Francqui Chair of the Belgian Francqui Foundation in 2020, and holds the first Bavarian AI Chair at LMU from 2020 on. She was Chair of the SIAM Activity Group on Imaging Sciences from 2018-2019 and Vice Chair of the new SIAM Activity Group on Data Science in 2021, and currently serves as Vice President-at-Large of SIAM. She is also the main coordinator of the Long-Term Programme on "Mathematics of Deep Learning" at the Isaac Newton Institute in 2021 and of the Research Focus "Next Generation AI" at the Center for Advanced Studies at LMU from 2021 to 2023.
Gitta Kutyniok's research work covers, in particular, the areas of applied and computational harmonic analysis, approximation theory, artificial intelligence, compressed sensing, frame theory, imaging sciences, inverse problems, machine learning, numerical analysis of partial differential equations, and applications to life sciences and telecommunication. She is primarily interested in developing mathematical methodologies and associated theories to solve application motivated problems. The most significant of Gitta Kutyniok's contributions is perhaps the introduction of the directional multiscale system of shearlets (www.ShearLab.org) and a comprehensive theoretical approach to analyze sparse regularizationof inverse problems using harmonic analysis and microlocal analysis. In 2015, she entered the area of machine learning, aiming to develop a theoretical foundation for deep learning, also for its application within mathematics. In this direction, some of her most well-known contributions are the analysis and construction of memory-optimal deep neural networks by using classical approximation theory as well as a theoretical analysis of deep neural networks and parametric partial differential equations. Another focus of hers is on optimal combinations of model- anddata-based approaches, where she, for instance, developed a state-of-the-art algorithm for the limited-angle computed tomography problem using a combination of deep neural networks and sparse regularization by shearlets.
Prof. Dr. Holger Rauhut has studied mathematics at the Technical University of Munich from 1996 until 2001, where he also obtained his doctoral degree in mathematics in 2004 with a thesis related to time-frequency and wavelet analysis. After a short postdoc at the University of Wroclaw in 2005, he moved to the Numerical Harmonic Analysis Group at the University of Vienna, where he obtained his habilitation degree in mathematics in 2008. In the same year, he became temporary associate professor ("Bonn Junior Fellow") at the Hausdorff Center for Mathematics at the University of Bonn. Since 2013 he is full professor for Mathematics at RWTH Aachen University leading the Chair for Mathematics of Information Processing. From 2016 to 2018 he was the spokesperson of the Department of Mathematics of RWTH and since 2018 he is member of the Senate of RWTH. His research interests include compressive sensing, applied harmonic analysis, random matrices and mathematics of deep learning. Together with Prof. Simon Foucart he authored a monograph entitled "A Mathematical Introduction to Compressive Sensing", which appeared in 2013. Since 2018 he serves as co-spokesperson, jointly with Prof. Dr. Gitta Kutyniok, of the DFG priority program SPP1798 "Compressive Sensing in Information Processing".
Dr. Robert J. Kunsch has studied Mathematics at the Friedrich Schiller University Jena from 2009 until 2014. His doctoral degree in mathematics was obained in 2017, also in Jena, the thesis dealing with Monte Carlo methods for high-dimensional function approximation. Spending two years as a postdoctoral researcher in Osnabrück, from 2019 on he is member of the Chair for Mathematics of Information Processing lead by Prof. Dr. Holger Rauhut at the RWTH Aachen University. His research interests comprise Monte Carlo methods as well as mathematics of deep learning.