Statistical Signal Processing for Modern High-Dimensional Data Sets

April 8, 2009 - Patrick Wolfe, Associate Professor of Electrical Engineering, Statistics and Information Sciences Laboratory, School of Engineering and Applied Science, Harvard University Modern science and engineering applications give rise to the vast quantities of high-dimensional data. This talk will provide a broad research perspective on the challenges and opportunities of drawing inferences from such data sets. For the large collections of sounds, images and networks acquired by modern sensing devices, traditional signal processing techniques singularly fail to scale, and new approaches are needed. Among the problems to be considered are forensic speech analysis, digital camera design and data reduction for large networks. Can we build practical solutions for these new contexts using the algorithms and tools of classical statistics?