Monday October 8 4:00 - 4:50 PM Kelley 1001
Raviv Raich Assistant Professor School of Electrical Engineering & Computer Science Oregon State University Flow Cytometry: Manifold Learning of High Dimensional Data The task of analyzing and processing high volumes of information poses a great challenge. We are interested in extracting a simple model that supports the complex data we observe to explain phenomena of interest. Geometry and more specifically manifolds offer means of explaining a low dimensional description of high dimensional data. One application of interest is Flow Cytometry, a technique that utilizes fluid dynamics to allow for individual identification of cells and statistical analysis of the sample as whole. In this presentation, we will demonstrate how learning Riemannian manifolds can be applied to Flow Cytometry for visualization, clustering, and classification of various cancer types. Biography: Raviv Raich joined the faculty in the School of Electrical Engineering and Computer Science at Oregon State University in Fall 2007. 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. Most recently, he was 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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