Workshop on Sparsity in Applied Mathematics and Statistics Workshop Announcement
In recent years the acquisition of big data sets on one hand and the increasing popularity of high-dimensional models on the other hand have intensified the interdisciplinary contacts between data sciences, machine learning, statistics, applied mathematics, computer science and signal processing. The learning or estimation of patterns and structures from massive observations in complex models is closely related to the solution of possibly large, ill posed or ill conditioned inverse problems. The understanding of graphical and structured models involves expertise in the algorithmic, numerical and statistical aspects. Regularisation of ill posed or ill conditioned problems is often based on an explicit or implicit assumption of sparsity, which can be imposed already at the recovery of the data, as in compressed sensing.
The goal of this workshop is to bring together several experts in the various fields of expertise.

Topics include:
Invited Speakers:

The organizers can be contacted at