Rick Chartrand was born in Winnipeg, Manitoba in 1971, and lived through 22 bitterly-cold winters and mosquito-infested summers before finally leaving.
Rick received the B.Sc.(Hons.) degree in Mathematics from the University of Manitoba in 1993, receiving the Governor General's Medal for the best graduating bachelor's student in the province of Manitoba. He received the M.A. and Ph.D. degrees in Mathematics from the University of California, Berkeley, in 1994 and 1999 respectively. His thesis work was in the area of Hilbert spaces of holomorphic functions, a field without useful applications, a fact he was once proud of. He held Assistant Professor positions at Middlebury College and the University of Illinois at Chicago before coming to Los Alamos National Laboratory in 2003 and beginning to undertake useful work. He is now a Technical Staff Member in the Theoretical Division.
Rick's current research is in the field of compressive sensing, working on both algorithms for sparse signal reconstruction and the mathematical justification for these methods. His particular focus has been on nonconvex optimization methods, demonstrating both that these approaches can recover signals from fewer methods than the more typical convex approaches, and that simple algorithms can be reliably successful, despite the presence of huge numbers of local minima. His paper with Wotao Yin presents test results that show successful reconstructions of sparse signals from fewer random measurements than any other method published to date.
Previous research interests include functional analysis and image processing.