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Sparsity and Modern Mathematical Methods
for High Dimensional Data

Programme

Guest speakers:

  • Ingrid Daubechies (Princeton University)
    1. Introduction to the world of wavelets, curvelets (and other -lets)
    2. Sparse expansions: what have we learned and where are we going?
  • Gitta Kutyniok (Universität Osnabrück, Osnabrück)
    1. Beyond Wavelets: Compactly Supported Shearlets
    2. Beyond Sparsity: Clustered Sparsity and Data Separation
  • Javier Portilla (Instituto de Optica, CSIC, Madrid)
    1. Sparse Approximation: A general discussion and a simple algorithm
    2. Efficient $\ell_0$-based sparse approximation using Parseval frames
    3. From approximation to estimation. Some image processing examples.
  • David Stork (Ricoh Innovations, Menlo Park)
    1. Did the great masters ``cheat'' using optics? Computer science, optics and art history confront a bold theory
    2. Computer graphics in the history and interpretation of art: Computer science, optics and art history confront a bold theory
  • Pierre Vandergheynst (EPFL, Lausanne)
    1. Wavelets on graphs via spectral theory
    2. Spread spectrum imaging techniques in MRI and Radio-interferometry: experimental promises
  • Dimitri Van De Ville (EPFL, Lausanne)
    1. Surfing the Brain: Wavelets and Sparsity for Functional Brain Imaging
    2. Steerable Wavelet Pyramids and Reconstruction from a Compact Multiscale Primal Sketch
  • Don Johnson (Rice University, Houston)
    1. Signal Processing and Analyzing Works of Art (slides)

Daily programme:

The daily programme is in the workshop guide book on the main page.

Slides (in chronological order):