Chartrand

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the University of Manitoba in 1993, receiving the Governor General's
the University of Manitoba in 1993, receiving the Governor General's
Medal for the best graduating bachelor's student in the
Medal for the best graduating bachelor's student in the
-
province of Manitoba.
+
province of Manitoba. He received the M.A. and Ph.D. degrees in
-
 
+
-
He received the M.A. and Ph.D. degrees in
+
Mathematics from the University of California, Berkeley, in 1994 and
Mathematics from the University of California, Berkeley, in 1994 and
-
1999 respectively.
+
1999 respectively. His thesis work was in the area of Hilbert spaces of
-
 
+
-
His thesis work was in the area of Hilbert spaces of
+
holomorphic functions, a field without useful applications, a fact he
holomorphic functions, a field without useful applications, a fact he
-
was once proud of.
+
was once proud of. He held Assistant Professor positions at
-
 
+
-
He held Assistant Professor positions at
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Middlebury College and the University of Illinois at Chicago before
Middlebury College and the University of Illinois at Chicago before
coming to Los Alamos National Laboratory in 2003 and beginning to
coming to Los Alamos National Laboratory in 2003 and beginning to
-
undertake useful work.
+
undertake useful work. He is now a Technical Staff Member in the Theoretical Division.
-
 
+
-
He is now a Technical Staff Member in the Theoretical Division.
+
Rick's current research is in the field of compressive
Rick's current research is in the field of compressive
sensing, working on both algorithms for sparse signal reconstruction
sensing, working on both algorithms for sparse signal reconstruction
-
and the mathematical justification for these methods.
+
and the mathematical justification for these methods. His particular focus has been on nonconvex optimization methods, demonstrating both
-
 
+
-
His particular focus has been on nonconvex optimization methods, demonstrating both
+
that these approaches can recover signals from fewer methods than the
that these approaches can recover signals from fewer methods than the
more typical convex approaches, and that simple algorithms can be
more typical convex approaches, and that simple algorithms can be
reliably successful, despite the presence of huge numbers of local
reliably successful, despite the presence of huge numbers of local
-
minima.
+
minima. His paper with Wotao Yin presents test results that show
-
 
+
-
His paper with Wotao Yin presents test results that show
+
successful reconstructions of sparse signals from fewer random
successful reconstructions of sparse signals from fewer random
measurements than any other method published to date.
measurements than any other method published to date.
Line 45: Line 33:
== Nonconvex Compressive Sensing ==
== Nonconvex Compressive Sensing ==
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[http://www.convexoptimization.com/TOOLS/Chartrand1.pdf Presented by Rick Chartrand with Valentina Staneva, Wotao Yin, & Kevin Vixie at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008]
+
[http://www.convexoptimization.com/TOOLS/Chartrand1.pdf Presented by Rick Chartrand (joint work with Valentina Staneva, Wotao Yin, & Kevin Vixie) at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008]

Revision as of 16:12, 7 August 2008

Rick Chartrand

Rick Chartrand, ca. 2008
Rick Chartrand, ca. 2008

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.


Nonconvex Compressive Sensing

Presented by Rick Chartrand (joint work with Valentina Staneva, Wotao Yin, & Kevin Vixie) at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008

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