In the last decade, sparse recovery techniques have proved increasingly popular for extracting low-dimensional features from high-dimensional data. Furthermore, there is now a rich mathematical theory underlying these techniques, often referred to as compressed sensing. In this talk, I will give an overview of compressed sensing and sparse recovery, highlighting both the main techniques and the fundamental mathematics. In the second half of the talk I will focus on a number of recent extensions and applications, with the aim being to demonstrate how to leverage additional benefits via more refined structured sparsity models.
Ben Adcock is an assistant professor at Simon Fraser University. He studied mathematics at the University of Cambridge, receiving his BA in 2005, his MMath in 2006, and his PhD in 2011. He held NSERC and PIMS postdoctoral fellowships at Simon Fraser University from 2010 to 2012, and was an assistant professor in the Department of Mathematics at Purdue University from 2012 to 2014, before returning to Canada in August of that year. He was the recipient of a Leslie Fox Prize in Numerical Analysis in 2011, an Alfred P. Sloan Research Fellowship in 2015 and the CAIMS-PIMS Early Career Award in 2017. His research interests include applied and computational harmonic analysis, numerical analysis and approximation theory.