Coding, Complexity, and Sparsity Workshop

August 5-7, 2013


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.

Previous incarnations

[ SPARC 2011 ] [ SPARC 2012 ]

Instructional Tutorials

These will be hour-long lectures designed to give students an introduction to the area.

  • Swastik Kopparty
  • Eric Price
  • Mary Wootters

Confirmed Speakers

Jin-Yi CaiUniversity of Wisconsin, Madison
Amit ChakrabartiDartmouth College
Piotr IndykMIT
Jonathan KelnerMIT
Valerie KingUniversity of Victoria
Swastik KoppartyRutgers University
Dick LiptonGeorgia Tech
Andrew McGregorUniversity of Massachusetts, Amherst
Jelani NelsonHarvard
Eric PriceMIT
Christopher RéStanford University
Ronitt RubinfeldMIT
Shubhangi SarafRutgers University
Adam SmithPennsylvania State University
David WoodruffIBM
Mary WoottersMichigan
Shuheng ZhouMichigan


Anna Gilbert -- S. Muthukrishnan -- Hung Q. Ngo -- Ely Porat -- Atri Rudra -- Martin Strauss


This material is based upon work supported by the National Science Foundation under Grant Numbers CCF-1161196 and CCF-1161233. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.