# Chartrand

### From Wikimization

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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 | + | |

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. | ||

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== Nonconvex Compressive Sensing == | == Nonconvex Compressive Sensing == | ||

- | [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 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.