In recent years exciting new developments in mathematics and computer science have opened up new domains of application for computational mathematics. These developments bring new challenges, for which new approaches and new tools must be developed. These draw not only from traditional linear-algebra-based numerical analysis or approximation theory, but also from information theory, graph theory, the geometry of Banach spaces, probability theory, and more. Often the features, patterns or structures of interest hidden in the data, are typically concentrated on subspaces or manifolds of
much smaller dimensions. Even if one has no extra a priori knowledge about which subspace or submanifold might carry the information of interest, the knowledge that it is of much smaller dimension helps in ``digging it out''. Taking advantage of this underlying sparsity lies at the heart of these new
developments. It is also the central tenet of compressed sensing and is presently seeing intense development in inverse problems as well.

This workshop will give young scientists in particular the opportunity to present their recent results on new mathematical methods for high-dimensional data and their applications, to a broad audience. In addition, a small number of invited speakers will present their research field in a more general way.

-sparse techniques in inverse problems and compressed sensing (theory, algorithms, applications, ...)

-wavelet-like transforms (shearlets, curvelets, use of non-regular grids, ...)

-statistical multi-resolution modeling and restoration of images (with applications in remote sensing, biomedical imaging, ...)

-analysis of multi-spectral data and the study of art
Effective start/end date17/09/09 → …

    Research areas

  • Mathematics

ID: 3336394