Graph Signal Processing: Fundamentals and Applications to Diffusion Processes

December 6th, Tuesday, 1:30-5pm


Prof. Antonio G. Marques, King Juan Carlos University, Spain
Prof. Alejandro Ribeiro, University of Pennsylvania, USA
Dr. Santiago Segarra, Massachusetts Institute of Technology, USA
Prof. Gonzalo Mateos, University of Rochester, USA


The tutorial consists of two parts of similar length: an introduction to the basics of Graph Signal Processing (GSP), which will review and illustrate main existing results, and the application of GSP-tools to distributed network processing and diffusion processes over networks. The first part introduces the field of GSP, motivates its usefulness via meaningful applications, and presents in a didactic yet concise manner its foundational concepts, which have been derived over the past five years. The second part focuses on contemporary results. We will first illustrate that GSP is well suited to model and study diffusion processes over networks. With this premise in mind, we revisit classical SP problems such as sampling, interpolation, system identification, and filtering. We first present the theoretical results and then discuss their implications for distributed and dynamic processing. Furthermore, we illustrate the utility of applying GSP to analyze dynamics on networks through a diverse gamut of applications from social sciences to biology, spanning well-established problems like consensus and emerging neuroscience challenges like brain state induction.


Antonio G. Marques received the Telecommunications Engineering degree and the Doctorate degree, both with highest honors, from the Carlos III University of Madrid, Spain, in 2002 and 2007, respectively. In 2007, he became a faculty of the Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain, where he currently develops his research and teaching activities as an Associate Professor. From 2005 to 2015, he held different visiting positions at the University of Minnesota, Minneapolis. In 2015 and 2016 he was a Visiting Scholar at the University of Pennsylvania. His research interests lie in the areas of communication theory, signal processing, and networking. His current research focuses on stochastic resource allocation wireless networks and smart grids, nonlinear network optimization, and signal processing for graphs. Dr. Marques has served the IEEE and the EURASIP in a number of posts (currently, he is an Associate Editor of the IEEE Signal Process. Letters and of the EURASIP J. on Advances in Signal Process.), and his work has been awarded in several conferences and workshops.

Alejandro Ribeiro received the B.Sc. degree in electrical engineering from the Universidad de la República, Uruguay, in 1998 and the M.Sc. and Ph.D. degree in electrical engineering from the Department of Electrical and Computer Engineering, the University of Minnesota, Minneapolis in 2005 and 2007. From 1998 to 2003, he was a member of the technical staff at Bellsouth Montevideo. After his M.Sc. and Ph.D studies, in 2008 he joined the University of Pennsylvania (Penn), Philadelphia, where he is currently the Rosenbluth Associate Professor at the Department of Electrical and Systems Engineering. His research interests are in the applications of statistical signal processing to the study of networks and networked phenomena. His current research focuses on wireless networks, network optimization, learning in networks, networked control, robot teams, and structured representations of networked data structures. Dr. Ribeiro received the 2014 O. Hugo Schuck best paper award, the 2012 S. Reid Warren, Jr. Award presented by Penn's undergraduate student body for outstanding teaching, the NSF CAREER Award in 2010, and student paper awards at the 2013 American Control Conference (as adviser), as well as the 2005 and 2006 International Conferences on Acoustics, Speech and Signal Processing. Dr. Ribeiro is a Fulbright scholar and a Penn Fellow.

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. and Ph.D. degrees in electrical and systems engineering from the University of Pennsylvania, Philadelphia, in 2014 and 2016. Since 2016, he has been working as a postdoctoral researcher with the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. His research interests include network theory, data analysis, machine learning, and graph signal processing. Dr. 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, the Best Student Paper Awards at the 2015 Asilomar Conference and the 2016 Statistical Signal Processing Workshop, and the Best Paper Award at the 2016 Sensor Array and Multichannel Signal Processing Workshop.

Gonzalo Mateos received the B.Sc. degree from Universidad de la República, Uruguay, in 2005, and the M.Sc. and Ph.D. degrees from the University of Minnesota, Twin Cities, in 2009 and 2011, all in electrical engineering. He joined the University of Rochester, Rochester, NY, in 2014, where he is currently an Assistant Professor with the Department of Electrical and Computer Engineering, as well as a member of the Goergen Institute for Data Science. During the 2013 academic year, he was a visiting scholar with the Computer Science Department at Carnegie Mellon University. From 2004 to 2006, he worked as a Systems Engineer at Asea Brown Boveri (ABB), Uruguay. Dr. Mateos received the Best Student Paper Award at the 2012 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), and was also a finalist of the Student Paper Contest at the 2011 IEEE DSP/SPE Workshop. His doctoral work has been recognized with the 2013 University of Minnesota's Best Dissertation Award (Honorable Mention) across all Physical Sciences and Engineering areas. His research interests lie in the areas of statistical learning from Big Data, network science, decentralized optimization, and graph signal processing, with applications in dynamic network health monitoring, social, power grid, and Big Data analytics. Dr. Mateos currently serves as Associate Editor for the IEEE Trans. on Signal Process. and the EURASIP J. on Advances on Signal Process.

GlobalSIP 2016 thanks the following for their support.