Biograph: James E. Fowler received the B.S. degree in computer and information science engineering and the M.S. and Ph.D. degrees in electrical engineering from The Ohio State University, Columbus, OH, USA, in 1990, 1992, and 1996, respectively.

In 1997, he held a postdoctoral assignment at the Universite de Nice-Sophia Antipolis, France, and, in 2004, he was a Visiting Professor at Telecom ParisTech, Paris, France. He is currently Billie J. Ball Professor and Interim Department Head of the Department of Electrical & Computer Engineering at Mississippi State University in Starkville, MS.

Dr. Fowler is the Editor-in-Chief of IEEE Signal Processing Letters. He was previously a Senior Area Editor for IEEE Transactions on Image Processing and Associate Editor for IEEE Transactions on Computational Imaging, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, and IEEE Signal Processing Letters. He is currently an Associate Editor for the EURASIP Journal on Image and Video Processing. He is a former chair of the Image, Video, and Multidimensional Signal Processing Technical Committee of the IEEE Signal Processing Society and is currently a member of the Computational Imaging Technical Committee of the IEEE Signal Processing Society. He was a General co-Chair of the 2014 IEEE International Conference on Image Processing, Paris, France, and the Speech, Image, and Video Processing track chair of the 2013 Asilomar Conference on Signals, Systems, and Computers. He is currently the publicity chair of the Data Compression Conference. He is a Fellow of the IEEE.

Title:Low-Rank and Sparse Representations in Signal Processing

Abstract: Many signal-processing problems of current interest can be cast as the separation of a low-rank signal of interest from a sparse signal of outliers. Such a low-rank/sparse representation (LRSR) has found extensive use across a myriad of signal-processing applications over the last decade. This talk reviews the foundational motivations for the coupling low-rank and sparsity constraints as well as the mathematical formulation and solution to such a framework. Several representative applications of LRSR are then overviewed, including recent results for foreground/background extraction of video, destriping of hyperspectral imagery, and unsupervised hyperspectral classification.