Compressed Sensing, Enriched

For an area to be successful in Applied Algorithms, several things have to come together: the math and theory should be novel and get the community's respect, the applications should solve a genuine problem or satisfy an important need, and researchers have to be creative to put it all together, engineer and deliver. Many researchers in (applied) algorithms have learned in the past couple of decades to do more than one of the above, if not all, and consequently, some of the applied algorithms areas have been successful (computational biology and streaming are two examples on my mind, but I am sure there are others).

Compressed Sensing is emerging as such a successful area. It has a pedigree of significant Math and theory of CS: this aspect has been talked about, blogged about, covered in media and we know great researchers bearing the torches. It has an incredible amount of applications. Rich Baraniuk and his terrific team have not only done the math and theory, but have also built things: cameras (award), MRI imagers, hyperspectral/radar imaging, DNA microarrays, and myraid others. This requires insight to figure out applications, creative adaptation of methods, tremendous engineering talent to execute them and a lot of thought and sweat. Others have joined in, and it is exciting to see signal processing, EE, biomedical and other researchers now growing the area even further by building hardware, software and useable ware.

ps: Rich gave an inspiring plenary talk at the von Neumann Symposium on Sparse Representations earlier this year.