This tutorial introduces Graph Signal Processing (GSP), an extension of traditional Digital Signal Processing (DSP) to data supported by graphs. GSP can process not only traditional DSP signals like time series or images, but also graph data. Applications include processing data from telecom networks, chemical networks, social networks and the marketing, corporate, financial, health care domains. Recently, GSP has been applied to smart power grid data and power system models. This tutorial provides an overview of GSP from first principles. We present the GSP companion model, a novel way to design and understand GSP concepts. Using this companion model, we present GSP as an intuitive, direct extension of DSP concepts. Examples will be provided for GSP applications in affordable, clean energy and smart grids.

Biography:

John Shi (jshi3@andrew.cmu.edu) has undergraduate degrees in Computer Engineering and Applied Mathematics from the University of Maryland (UMD) and a Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University. He was a post-doc at Carnegie Mellon University (CMU). His Ph.D. thesis is titled, “A Dual Domain Approach to Graph Signal Processing.” Topics from this research will be included in this tutorial.

<<<Back to Tutorials<<<