Event Detail

Event Type: 
Probability Seminar
Thursday, February 21, 2008 - 06:00
Kidder 364

Speaker Info

School of Electrical Engineering and Computer Science, Oregon State University

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.