Dr. James Nagy

Professor, Mathematics and Computer Science Department, Emory University 

"Efficient Iterative Methods for Large Scale Inverse Problems"

Abstract:

Ill-posed problems arise in many image processing applications, including microscopy, medicine and astronomy.  Iterative methods are typically recommended for these large scale problems, but they can be difficult to use in practice.  In this talk we describe a hybrid approach that combines the Golub-Kahan bidiagonalization algorithm with Tikhonov regularization and a weighted generalized cross validation scheme.  We also show how this method can be used as an effective scheme for choosing regularization parameters for certain nonlinear inverse problem.  Applications from image processing illustrate the effectiveness of the resulting numerical schemes.

This is joint work with Julianne Chung, University of Maryland.

Bio:

James Nagy is a Professor of Mathematics and Computer Science at Emory University.  He received his Ph.D. in Applied Mathematics from North Carolina State University in 1991.  Before joining Emory University in 1999 he had postdoctoral research fellowships with the IMA at the University of Minnesota, with the NSF at the University of Maryland, and was on the faculty at Southern Methodist University.  He is on the editorial boards of SIAM Journal on Scientific Computing (SISC), SIAM Journal on Matrix Analysis and Applications (SIMAX), and the SIAM Book Series "Fundamentals of Algorithms".  His research interests include numerical linear algebra, structured matrix computations, and numerical solution of inverse problems in image processing. Among his many publications includes the recent SIAM book, "Image Deblurring: Matrices, Spectra and Filtering", which is co-authored with Per Christian Hansen and Dianne O'Leary. Visit Dr. Nagy's website.

Please join us for a reception preceding the seminar outside Klaus 1324, beginning at 1:30pm.

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