Applied Harmonic Analysis and Data Science

Oberwolfach Reports, vol. 18, pp. 3007–3066

Abstract

Data science has become a field of major importance for science and technology nowadays and poses a large variety of challenging mathematical questions. The area of applied harmonic analysis has a significant impact on such problems by providing methodologies both for theoretical questions and for a wide range of applications in signal and image processing and machine learning. Building on the success of three previous workshops on applied harmonic analysis in 2012, 2015 and 2018, this workshop focused on several exciting novel directions such as mathematical theory of deep learning, but also reported progress on long-standing open problems in the field.

Authors 4

1. Ingrid C. Daubechies

   Duke University

   Affiliation as printed

   Duke University, Durham, USA

2. Gitta Kutyniok

   Ludwig-Maximilians-Universität München

   Affiliation as printed

   Ludwig-Maximilians-Universität München, Germany

3. Holger Rauhut Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen, Germany

4. Thomas Strohmer

   University of California, Davis

   Affiliation as printed

   University of California at Davis, USA