Emmanuel Candés | Research in the Big Data Era

Claude Shannon Centennial Symposium
Rackham Building Amphitheatre
University of Michigan, Ann Arbor

Emmanuel Candés talks about the reproducibility of scientific research in the Big Data Era and what statistics can offer

Emmanuel Jean Candès is a professor of mathematics, statistics, and electrical engineering at Stanford University, where he is also the Barnum-Simons Chair in Mathematics and Statistics. Candès' early research concerned nonlinear approximation theory. In his Ph.D. thesis, he developed generalizations of wavelets called curvelets and ridgelets that were able to capture higher order structures in signals. This work has had significant impact in image processing and multiscale analysis, and earned him the Popov prize in approximation theory in 2001.
In 2006, Candès wrote a paper with Terence Tao that kicked off the field of compressed sensing: the recovery of sparse signals from a few carefully constructed, and seemingly random measurements. Many researchers have since contributed to this field, which has brought us the idea of a camera that can record pictures while needing only one sensor, and tools for designing distributed sensors that can communicate cheaply.
http://statweb.stanford.edu/~candes/