Seminar: ECE Faculty Candidate
Wednesday February 28 11:00 - 11:50 AM Kelley 1007 Raviv Raich Postdoctoral Research Fellow Department of Electrical Engineering and Computer Science University of Michigan Discovering Structure in High Dimensional Data Many algorithms in signal and image processing aim to extract and analyze information from measured data; a reliable model can assist in this task. In a non parametric setting, no a priori assumptions are made and no model is assigned to describe the data. Recent efforts are directed towards exploring and utilizing data structure. Think, for example, of a high-dimensional feature vector whose features are correlated and exhibit linear dependence. The features lies on a lower-dimensional hyperplane and can be described and processed using a lower-dimensional representation. Manifolds offer the capability to extend these ideas from a linear to a nonlinear setting. In manifold learning, we assume that the data lies on a low-dimensional manifold in a high-dimensional space. The dimension and structure of the manifold can be learned from the data to assist in various learning tasks. Under the sparse signal framework, signals of interest can be represented using a small subset of elements from a large dictionary. While the dictionary can span the entire high-dimensional space, sparse signals reside in a small portion of it. This assumption can be incorporated into many signal processing problems to yield innovative algorithms that can analyze the data more accurately. This talk presents an extension of manifold learning techniques to supervised and semi-supervised learning with applications to medical diagnosis, hyper spectral imaging, and localization in sensor networks. Additionally, novel sparse reconstruction algorithms with application to magnetic resonance force microscopy will be presented. Biography: Raviv Raich received the B.Sc. and M.Sc. degrees in electrical engineering from Tel-Aviv University, Tel-Aviv, Israel, in 1994 and 1998, respectively and the Ph.D. degree in electrical engineering from Georgia Institute of Technology, Atlanta, Georgia, in 2004. Between 1999 and 2000, he worked as a researcher with the communications team, Industrial Research Ltd., Wellington, New Zealand. Currently, he is a postdoctoral research fellow at the University of Michigan, Ann Arbor, Michigan. His main research interest is in statistical signal processing with specific focus on manifold learning, sparse signal reconstruction, and adaptive sensing. Other research interests lie in the area of statistical signal processing for communications, estimation and detection theory.
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