HCM Workshop: Synergies between Data Science and PDE Analysis

Date: June 13 - 17, 2022

Venue: Lipschitz lecture hall, Mathematics Center, Endenicher Allee 60, 53115 Bonn

Organizer: Leon Bungert (Bonn), Franca Hoffmann (Bonn)



The current success story of data science calls for interpretable machine learning methods. For instance, in the context of sampling, graph-based learning or neural networks, PDE-based methods have proven to be both efficient and apt for mathematical analysis.

This workshop brings together experts and young researchers in these two vibrant fields and aims to create a stimulating environment for exchanging scientific ideas and establishing new collaborations.


Keynote Speakers:

  • Eldad Haber (University of British Columbia, Canada)
  • Gitta Kutyniok (Ludwig-Maximilians-Universität München, Germany)
  • Michael Unser (EPFL, France)


  • Matthias J Ehrhardt (University of Bath, UK)
  • Tamara Grossmann (University of Cambridge, UK)
  • Bamdad Hosseini (University of Washington, Seattle, USA)
  • Arnulf Jentzen (University of Muenster, Germany)
  • Anna Korba (ENSAE Paris, France)
  • Yury Korolev (University of Cambridge, UK)
  • Lisa Maria Kreusser (University of Bath, UK)
  • Carlo Marcati (Università degli Studi di Pavia , Italy)
  • Nikolas Nüsken (University of Potsdam, Germany)
  • Josiah Park (Texas A&M University, USA)
  • Phillipp Petersen (Universty of Vienna, Austria)
  • Clarice Poon (Universty of Bath, UK)
  • Tim Roith (Friedrich-Alexander Universität Erlangen-Nürnberg, Germany)
  • Dejan Slepcev (Carnegie Mellon University, Pittsburgh, USA)
  • Matthew Thorpe (University of Manchester, UK)
  • Nicolas Garcia Trillos (University of Wisconsin, Madison, USA)
  • Urbain Vaes (Inria Paris. France)
  • Harini Veeraraghavan (Memorial Sloan Kettering Cancer Center, USA)
  • Stephan Wojtowytsch (Texas A&M University, College Station, USA)
  • Yunan Yang (ETH Zürich, Switzerland)


This workshop is organized in tandem with the Hausdorff School “Foundational Methods in Machine Learning”.



Please find the abstract book here.