When encoding or reconstructing a vector using an iterative algorithm, a natural approach is to take the best or biggest approximation at each iteration. Such techniques are referred to as greedy algorithms. The theory of compressed sensing is concerned with encoding and reconstructing vectors which are sparsely represented with respect to a given basis. Kevin Ford will present a series of talks on deterministic construction of matrices with the restrictive isometry property. There will be a second series of talks devoted to greedy algorithms and bases.

The program will include an introductory course by Mark Sapir on coarse embeddings and their applications to geometric group theory, and a series of lectures by Gilles Godefroy on the recent work of the late Nigel Kalton on the coarse classification of Banach spaces.