Applied Harmonic Analysis and Data Science

  • Ingrid Daubechies

    Duke University, Durham, USA
  • Gitta Kutyniok

    Ludwig-Maximilians-Universität München, Germany
  • Holger Rauhut

    RWTH Aachen, Germany
  • Thomas Strohmer

    University of California at Davis, USA


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.

Cite this article

Ingrid Daubechies, Gitta Kutyniok, Holger Rauhut, Thomas Strohmer, Applied Harmonic Analysis and Data Science. Oberwolfach Rep. 18 (2021), no. 4, pp. 3007–3066

DOI 10.4171/OWR/2021/55