Efficient and effective transmission, storage, and retrieval of information on a large-scale are among the core technical problems in the modern digital revolution. The massive volume of data necessitates the quest for mathematical and algorithmic methods for efficiently describing, summarizing, synthesizing, and, increasingly more critical, deciding when and how to discard data before storing or transmitting it. Such methods have been developed in two areas: coding theory, and sparse approximation (SA) (and its variants called compressive sensing (CS) and streaming algorithms). Coding theory and computational complexity are both well established fields that enjoy fruitful interactions with one another. On the other hand, while significant progress on the SA/CS problem has been made, much of that progress is concentrated on the feasibility of the problems, including a number of algorithmic innovations that leverage coding theory techniques, but a systematic computational complexity treatment of these problems is sorely lacking. The workshop organizers aim to develop a general computational theory of SA and CS (as well as related areas such as group testing) and its relationship to coding theory. This goal can be achieved only by bringing together researchers from a variety of areas. We will have several tutorial lectures that will be directed to graduate students and postdocs.
These will be hour-long lectures designed to give students an introduction to the area.
|Jin-Yi Cai||University of Wisconsin, Madison|
|Amit Chakrabarti||Dartmouth College|
|Valerie King||University of Victoria|
|Swastik Kopparty||Rutgers University|
|Dick Lipton||Georgia Tech|
|Andrew McGregor||University of Massachusetts, Amherst|
|Christopher Ré||Stanford University|
|Shubhangi Saraf||Rutgers University|
|Adam Smith||Pennsylvania State University|