9:45 AM - 12:15 PM
Room: University Ballroom A
For Part II, see MS21
A fundamental problem in science and technology concerns the recovery of an object---a digital signal or image---from incomplete measurements. The examples of such situations are numerous, ranging from the sampling of continuous signals in signal processing, to the measurement of the two-dimensional frequency spectrum of an image as in biomedical imaging. Is it possible to reconstruct an image, or at least certain types of images, from vastly undersampled data? If so, how?
The proposed workshop is about an emerging mathematical theory showing that it is surprisingly possible to reconstruct certain types of signals/images accurately, and sometimes even exactly, from a limited number of measurements; that is, it applies to signals or images known to be structured in the sense that they are sparse or compressible. This means that the unknown object depends upon a smaller number of unknown parameters, with only a few significant entries in some fixed representation.
This theory has some significant implications in many fields in the applied sciences and engineering, and especially in information theory, coding theory, digital signal processing and sensor networks to name just a few; the speakers will address the many opportunities in these fields.
This minisymposium will bring together applied mathematicians and engineers at the forefront of research in this new field. For example, there are several research teams which are building completely new devices which would collect the kind of incoherent measurements the theory suggests, and the workshop will include talks on such recent hardware developments.
Organizer:
Emmanuel Candes
California Institute of Technology