Trzasko
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(New page: = Joshua D. Trzasko = Joshua Trzasko was born February 4, 1981, in Yonkers, NY and grew up in the nearby town of Somers. After one year at Boston University, he transferred to Cornell ...) |
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= Joshua D. Trzasko = | = Joshua D. Trzasko = | ||
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Joshua Trzasko was born February 4, 1981, in Yonkers, NY and grew up in the nearby town of Somers. | Joshua Trzasko was born February 4, 1981, in Yonkers, NY and grew up in the nearby town of Somers. | ||
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Joshua's research focuses on the theory and application of sparse representations in medical imaging including Compressive Sensing methods for MRI, statistical models for MR elastography, and dose reduction techniques in X-ray Computed Tomography (CT). | Joshua's research focuses on the theory and application of sparse representations in medical imaging including Compressive Sensing methods for MRI, statistical models for MR elastography, and dose reduction techniques in X-ray Computed Tomography (CT). | ||
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| + | == Advances in Compressive Sensing for MRI == | ||
| + | [http://www.convexoptimization.com/TOOLS/SIAM_IS08_short2.ppt Presented by Joshua Trzasko with Armando Manduca at the SIAM Conference on Imaging Science 2008, San Diego, California, July 7, 2008] (15MByte) | ||
Current revision
Joshua D. Trzasko
Joshua Trzasko was born February 4, 1981, in Yonkers, NY and grew up in the nearby town of Somers.
After one year at Boston University, he transferred to Cornell University in 2000 to study Electrical and Computer Engineering, specializing in signal and image processing.
In the summer of 2002 he was an intern at General Electric's Global Research Center in Niskayuna, NY in the Magnetic Resonance Imaging (MRI) division.
Joshua graduated cum laude from Cornell in 2003 with a BS.
Since leaving Cornell, he has been working towards his Ph.D. in Biomedical Engineering under Armando Manduca, Ph.D. in the Biomathematics group at Mayo Clinic in Rochester, MN.
Joshua's research focuses on the theory and application of sparse representations in medical imaging including Compressive Sensing methods for MRI, statistical models for MR elastography, and dose reduction techniques in X-ray Computed Tomography (CT).