Graph Signal Processing: Fundamentals and Applications to Diffusion
Antonio G. Marques, King Juan Carlos University, Spain Prof.
Alejando Ribeiro, University of Pennsylvania, USA
Segarra, University of Pennsylvania, USA
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.
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. 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 ITBA’s 2011 award to the
best undergraduate thesis 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.
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.