University of Michigan Ann Arbor, MI Theme: 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. Speakers Instructional Tutorials: These will be hour-long lectures designed to give students an introduction to the area. Anna Gilbert, Compressive sensing Brett Hemenway, Coding theory Shachar Lovett, Pseudorandomness Andrew McGregor, Data stream algorithms Organizers Anna Gilbert, Martin Strauss, Atri Rudra, Hung Ngo, Ely Porat, S. Muthukrishnan Sponsors Department of Mathematics, Institute for Mathematics and its Applications (IMA), NSF, and Michigan Math Journal For website problems, contact Webmaster