Applied Harmonic Analysis and Data Science
Ingrid Daubechies
Duke University, Durham, USAGitta Kutyniok
Ludwig-Maximilians-Universität München, München, GermanyHolger Rauhut
Ludwig-Maximilians-Universität München, München, Germany
Abstract
Data science is 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 machine learning, as well as in in signal and image processing. Building on the success of four previous workshops on applied harmonic analysis in 2012, 2015, 2018, 2021, this workshop focused on several exciting directions, such as mathematical theory of deep learning, phase-retrieval time-frequency analysis, and sampling on t-design curves, and discussed open problems in the field.
Cite this article
Ingrid Daubechies, Gitta Kutyniok, Holger Rauhut, Applied Harmonic Analysis and Data Science. Oberwolfach Rep. 21 (2024), no. 2, pp. 1163–1226
DOI 10.4171/OWR/2024/21