Title: Recent Developments in Compressed Sensing

Abstract: Compressed sensing refers to the reconstruction of high-dimensional
but low-complexity objects from a limited number of measurements.
Examples include the recovery of high-dimensional but sparse vectors, and
the recovery of high-dimensional but low-rank matrices, which includes the
so-called partial realization problem in linear control theory. Much of
the work to date focuses on probabilistic methods, which are CPU-intensive and
have high computational complexity. In contrast, deterministic methods
are far faster in execution and more efficient in terms of storage.
Moreover, deterministic methods draw from many branches of mathematics,
including graph theory and algebraic coding theory. In this talk a brief
overview will be given of such recent developments.

Biography: Mathukumalli Vidyasagar
received his B.S., M.S. and Ph.D. degrees from the University of Wisconsin in
1965, 1967, and 1969. During his nearly fifty-year career, he has worked
in a number of research areas including control theory, robotics, statistical
learning theory, computational cancer biology, and most recently, compressed
sensing. He has received a number of awards in recognition of his
research contributions, including the IEEE Technical Field Award in Control Systems,
and Fellowship of The Royal Society, the world's
oldest scientific society.