Data Assimilation and Reduced Modeling for High Dimensional Problems

CIRM, Luminy, France
July 19-August 27, 2021


This summer school will be the opportunity to bring together students, academic and industrial researchers, and people working on Data Assimilation and Reduced Modeling and its applications to High-Dimensional Data. It will include five courses at Master/PhD level (no background required) in domains such as approximation theory, parametric PDEs, bayesian and deterministic data assimilation and filtering, deterministic and stochastic optimization. The mathematical foundations of this event lie between probability, statistics, numerical analysis, optimization, image and signal processing.


  • Start: Monday June 19 at 9.00 (GMT+2)
  • End: Friday June 23 at 17.30 (GMT+2)
  • Detailed schedule: here .
  • Change in the schedule for Thursday: J. Schmidt-Hieber and E. Moulines swap the time of their lecture. J. Schidt-Hieber will thus speak at 10.00 and E. Moulines will speak at 14.00.

Speakers and Courses

  • Approximation of multivariate functions: reduced modeling and recovery from uncomplete measurements.
    Material: [Abstract] [Slides] [Notebook]
    Lecturer: Albert Cohen (Sorbonne University)
    Lecturers hands-on session: Matthieu Dolbeault and Agustin Somacal (Sorbonne University)
  • Bayesian methods for inverse problems
    Material: [Abstract] [Slides] [Notebook] [Extra file for the notebook]
    Lecturer: Masoumeh Dashti (University of Sussex)
    Lecturer hands-on session: Tanja Zerenner (University of Sussex)
  • Stochastic Approximation
    Material: [Slides Part 1] [Slides Part 2] [Code]
    Lecturer: Eric Moulines (Ecole Polytechnique)
    Lecturer hands-on session: Aymeric Dieuleveut (Ecole Polytechnique)
  • Approximation and learning with tree tensor networks
    Material: [Abstract] [Slides] [Notebook] [Library Tensap]
    Lecturer: Anthony Nouy (Ecole Centrale de Nantes)
    Lecturer hands-on session: Mazen Ali (Ecole Centrale de Nantes)
  • Bayesian data assimilation and filtering
    Material: [Abstract] [Slides Part 1] [Slides Part 2] [Notebook]
    Lecturer: Claudia Schillings (Mannheim University)
    Lecturer hands-on session: Matei Hanu (Mannheim University)
  • Statistical analysis of machine learning methods
    Material: [Abstract] [Slides Part 1] [Slides Part 2] [Code] [Data set (26 Mb)] [README]
    Lecturer: Johannes Schmidt-Hieber (University of Twente)
    Lecturer hands-on session: Thijs Bos (University of Leiden)