Data Science: Rethinking DSP
Speaker: José M. F. Moura, Carnegie Mellon University
Department: Electrical Engineering
Location: Engineering Quadrangle B205
Date/Time: Friday, June 6, 2014, 1:30 p.m. - 2:30 p.m.
Chris Anderson* titled provocatively his 06.23.08 piece The End of Theory: The Data Deluge Makes the Science Method Obsolete. Data (big), computers (cloud), storage (vast), bandwidth (massive) and Google (or the likes) will find the correlations that will save the day. No (need for) causation. May be; or we might still try to explain it. Data is big, comes from all sorts of sourcessocial, business, urban, physical, biological, molecular. If we capture the relations among data through (arbitrary) graphs, we show how the big data challenge can be cast in the familiar setting of everyones beloved DSP, and how traditional models extend to the unstructured settings of big data. This talk will overview our progress so far extending to data defined on graphs (graph signals) traditional signal processing concepts including shifting, frequency, filtering, convolution, spectral representation, filters frequency response, linear transforms like the discrete Fourier transform. We illustrate with data drawn from social networks and the World Wide Web.
Work with Dr. Aliaksei Sandryhaila and graduate student Jonathan Mei.
*Editor in Chief of Wired Magazine.