Post date: 25-feb-2017 8:30:29
Title: Graph Signal Processing Tools for Topology Identification
Date: 24-June-2016
Aula:
Material: pdf slides, videos
Speakers: Santiago Segarra (UPenn)
Santiago Segarra received the B.Sc. degree in industrial engineering with highest honors (Valedictorian) from the Instituto Tecnológico de Buenos Aires (ITBA), Argentina, in 2011 and the M.Sc. degree in electrical engineering from the University of Pennsylvania, Philadelphia, in 2014. Since 2011, he has been working towards the Ph.D. degree in the Department of Electrical and Systems Engineering at the University of Pennsylvania. His research interests include network theory, data analysis, machine learning, and graph signal processing. Mr. Segarra received the ITBA's 2011 Best Undergraduate Thesis Award in industrial engineering, the 2011 Outstanding Graduate Award granted by the National Academy of Engineering of Argentina, and the Best Student Paper Award at the 2015 Asilomar Conference.
Abstract:
A network can be understood as a complex system formed by multiple nodes, where global network behavior arises from local interactions between connected nodes. Often, networks have intrinsic value and are themselves the object of study. In other occasions, the network defines an underlying notion of proximity or dependence, but the object of interest is a signal defined on top of the graph. This is the matter addressed in the field of graph signal processing (GSP). Graph-supported signals appear in many engineering and science fields such as gene expression patterns defined on top of gene networks and the spread of epidemics over social networks. Transversal to the particular application, the philosophy behind GSP is to advance the understanding of network data by redesigning traditional tools originally conceived to study signals defined on regular domains and extend them to analyze signals on the more complex graph domain. In this talk, we will introduce the main building blocks of GSP and illustrate the utility of these concepts. Our focus will be on graph topology identification from the observation of graph signals.