February Fourier Talks 2015

The central goal of compressed sensing is to capture attributes of a signal using very few measurements. In most work to date this broader objective is exemplified by the important special case of classification or reconstruction from a small number of linear measurements. In the first part of this talk we use wireless information theory to derive fundamental limits on compressive classification, on the maximum number of classes that can be discriminated with low probability of error and on the tradeoff between the number of classes and the probability of misclassification. In the second part we describe how to use information theory to guide the design of linear measurements by maximizing mutual information between the measurements and the statistics of the source.

Robert Calderbank is Director of the Information Initiative at Duke University, where he is Professor of Mathematics and Electrical Engineering. Prior to joining Duke as Dean of Natural Sciences in 2010, he directed the Program in Applied and Computational Mathematics at Princeton University. Prior to joining Princeton in 2004 he was Vice President for Research at AT&T, in charge of what may have been the first industrial research lab where the primary focus was Big Data. Professor Calderbank is well known for contributions to voiceband modem technology, to quantum information theory, and for co-invention of space-time codes for wireless communication. His research papers have been cited more than 30,000 times and his inventions are found in billions of consumer devices. Professor Calderbank was elected to the National Academy of Engineering in 2005 and has received a number of awards, including the 2013 IEEE Hamming Medal for his contributions to information transmission, and the 2015 Claude E. Shannon Award.