[Todos CMAT] Fwd: Charla de G. Mateos (Univ. of Rochester) - **martes 19/12 - 16hs**

Rafael Potrie rpotrie en cmat.edu.uy
Sab Dic 16 11:03:09 -03 2017

---------- Forwarded message ----------
From: Mauricio Delbracio <mdelbra en fing.edu.uy>
Date: 2017-12-16 10:56 GMT-03:00
Subject: Fwd: Charla de G. Mateos (Univ. of Rochester) - **martes 19/12 -
To: Rafael Potrie <rafaelpotrie en gmail.com>

---------- Forwarded message ----------
From: Mauricio Delbracio <mdelbra en fing.edu.uy>
Date: Mon, Dec 11, 2017 at 10:33 AM
Subject: Charla de G. Mateos (Univ. of Rochester) - **martes 19/12 - 16hs**

Tenemos el agrado de tener de visita a Gonzalo Mateos, profesor del
Dept. of ECE y del Goergen Institute for Data Science de la University
of Rochester. Para los que no lo conocen Gonzalo es egresado de
nuestra universidad, ex docente del iie y gran amigo de la casa.

La charla se titula "Network Topology Inference from Spectral
Templates" y se realizará el *martes 19/12 a las 16hs*
Salón 502 - Azul (5to. piso FING)

Abajo +información. Favor reenviar a interesados.



Network Topology Inference from Spectral Templates

Gonzalo Mateos  -- Dept. of ECE and Goergen Institute for Data
Science, University of Rochester

Advancing a holistic theory of networks necessitates fundamental
breakthroughs in modeling, identification, and controllability of
distributed network processes – often conceptualized as signals defined
on the vertices of a graph. Under the assumption that the signal
properties are related to the topology of the graph where they are
supported, the goal of graph signal processing (GSP) is to develop
algorithms that fruitfully leverage this relational structure, and can
make inferences about these relationships when they are only partially

After presenting the fundamentals of GSP, we leverage these ideas to
address the problem of network topology inference from graph signal
observations. It is assumed that the unknown graph encodes direct
relationships between signal elements, which we aim to recover from
observable indirect relationships generated by a diffusion process on
the graph. The innovative approach is to consider the Graph Fourier
Transform of the acquired signals associated with an arbitrary graph
and, among all the feasible networks, search for one that endows the
resulting transforms with target spectral properties and the sought
graph with appealing physical characteristics such as sparsity.
Leveraging results from GSP and sparse recovery, efficient topology
inference algorithms with theoretical guarantees are put forth.
Numerical tests corroborate de effectiveness of the proposed
algorithms when used to recover social and structural brain networks
from synthetically-generated signals, as well as to identify the
structural properties of proteins.

Gonzalo Mateos earned the B.Sc. degree from Universidad de la
Republica, 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. 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 received the
2017 IEEE Signal Processing Society Young Author Best Paper Award (as
senior co-author) as well as the Best Student Paper Award at the 2012
IEEE Workshop on Signal Processing Advances in Wireless Communications
(SPAWC) and the 2016 IEEE Statistical Signal Processing (SSP) Workshop
(as senior co-author). 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.

Rafael Potrie
rafaelpotrie en gmail.com
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