Follow-up Workshop "Mathematics of Data Science"

Date: April 27 - May 01, 2020 (please note: 1st May is a public holiday in Germany)
Venue: HIM lecture hall, Poppelsdorfer Allee 45, Bonn
Organizers: Massimo Fornasier (München), Mauro Maggioni (Baltimore), Holger Rauhut (Aachen)

This meeting is a follow-up workshop to the Trimester Program "Mathematics of Signal Processing".

Signal and data processing as well as machine learning and artificial intelligence (AI) -- all subsumed under the term Data Science -- have sparked technological innovation and are changing our society on many levels. The palette of conceivable applications of these data analysis methods is almost unlimited and we expect that this general-purpose technology will fundamentally change the world we live in. Examples of application areas include (personalized) medicine, optimization of chemical reactions, mechanical engineering, security, agriculture, energy, climate modeling, astro- and particle physics, economics, operations research and portfolio optimization, language processing (speech recognition, translation, speech synthesis), self-driving cars and robotics. Mathematical methods of signal processing are used in many technological devices such as mobile phones, digital cameras, medical, radar and chemical/physical sensors/detectors and more.

The development of mathematical and algorithmic methods for signal processing and machine learning has been the basis for technological breakthroughs of recent years and will continue to drive innovation in the future. The analysis of their potential and reliability is of utmost scientific and societal relevance. From a scientific point of view, signal processing and machine learning have been a constant source for interesting and challenging mathematical problems over the last decades, often leading to new ideas and directions in mathematics. The solution of these problems has provided new and improved approaches and methods to tackle practical signal processing problems, frequently paving the way for significant advances in technology and in the natural sciences.

The workshop will report on recent advances in the mathematics of data science. Particular topics of interest include deep learning, compressive sensing, signal processing and machine learning on graphs as well as applied harmonic analysis.