A series of thematic workshops in memory
of Alex B. Gershman
STATOS Thematic Workshop 2018
Graph Signal Processing
Friday, September 7, 2018
2pm – 6:30pm
(right after EUSIPCO 2018)
Location:
Centro Congressi di Confindustria
– Auditorium della Tecnica
Via dell’Astronomia 30, 00146, Rome,
Italy.
(venue
oft he EUSIPCO)
Call for participation:
Registration at:
http://statos2018.eventbrite.com
Organization committee:
General Chairs:
Maria Sabrina Greco, University of Pisa
Geert Leus, Delft
Technical Program
Prof. Sergio
Barbarossa
Time: 2:00 pm – 3:00 pm
Title: „Living in a
World of Multiway and Uncertain Relations: Signal Processing over Discrete Sets“
CV
Sergio Barbarossa (S’84–M’88–F’12) received the M.S. degree and Ph.D.
degree in EE from the Sapienza University of Rome, Rome, Italy, where he is currently
a Full Professor. He has held visiting positions at the Environmental Research
Institute of Michigan (’88), University of Virginia (’95, ’97), and University
of Minnesota (’99). He has been the scientific coordinator of several EU
projects on wireless sensor networks, small cell networks, and distributed
mobile cloud computing. He is currently the Technical Manager of the H2020
Europe/Japan project 5G-Mi-Edge. His current research interests include the
area of graph signal processing, distributed optimization, millimeter wave
communications, mobile edge computing, and machine learning. He is an EURASIP
Fellow and he has been an IEEE Distinguished Lecturer. He received the 2000 and
2014 IEEE Best Paper Awards from the IEEE Signal Processing Society and the
2010 Technical Achievements Award from the EURASIP
Gonzalo Mateos
Buckstein
Time: 3:00 pm –
4:00 pm
Title: “Network topology inference from spectral templates”
Abstract
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 observed.
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.
CV
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. He currently
serves as Associate Editor for the IEEE Transactions on Signal Processing, the
EURASIP Journal on Advances in Signal Processing, and is a member of the IEEE
SigPort Editorial Board. Dr. Mateos received the NSF CAREER Award in 2018, the
2017 IEEE Signal Processing Society Young Author Best Paper Award (as senior
co-author), and the Best Paper Awards at ICASSP 2018, SSP Workshop 2016, and
SPAWC 2012. 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.
Prof. Dimitri
Van De Ville
Time: 4:30 pm –
5:30 pm
Title: “Graph Signal Processing Opens New
Perspectives for Human Brain Imaging”
Abstract
State-of-the-art magnetic resonance imaging (MRI) provides unprecedented
opportunities to study brain structure (anatomy) and function (physiology).
Based on such data, graph representations can be built where nodes are
associated to brain regions and edge weights to strengths of structural or
functional connections. In particular, structural graphs capture major neural
pathways in white matter, while functional graphs map out statistical
interdependencies between pairs of regional activity traces. Network analysis
of these graphs has revealed emergent system-level properties of brain
structure or function, such as efficiency of communication and modular
organization.
In this talk, graph signal processing (GSP) will be presented as a novel
framework to integrate brain structure, contained in the structural graph, with
brain function, characterized by activity traces that can be considered as
time-dependent graph signals. Such a perspective allows to define novel
meaningful graph-filtering operations of brain activity that take into account
the anatomical backbone. For instance, we will show how activity can be
analyzed in terms of being aligned versus liberal with respect to brain
structure, or how additional prior information about cognitive systems can be incorporated.
The well-known Fourier phase randomization method to generate surrogate data
can also be adapted to this new setting. Finally, recent work will highlight
how the spatial resolution of this type of analyses can be increased to the
voxel level, representing a few ten thousands of nodes.
CV
Dimitri Van De Ville received his M.S. and Ph.D. degrees in Computer
Science from Ghent University, Belgium in 1998 and 2002, respectively. From
2002 to 2005, he was a post-doctoral fellow at the Biomedical Imaging Group of
Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL),
Switzerland. In 2005, he became responsible for the Signal Processing Unit at
the University of Geneva (UniGE) and Geneva University Hospital (HUG) as part
of the Centre d’Imagerie BioMédicale (CIBM), a large imaging initiative of the
Lemanic academic institutions. In 2009, he was awarded an SNSF professorship
and he started a joint tenure-track professorship at the UniGE (Department of
Radiology and Medical Informatics, Faculty of Medicine) and the EPFL (Institute
of Bioengineering, School of Engineering). Since 2015 he’s Associate Professor
of Bioengineering at both institutions and his lab is located at the newly
established Campus Biotech in Geneva.
He has published more than 100 journal papers on signal and image
processing, including on wavelet theory and network science, and their
application to the biomedical field, in particular functional brain imaging.
Recent work on dynamic functional connectivity included evidence that
resting-state functional networks can be disentangled in terms of their
temporal overlap, which showed a more complete picture of dynamic organization
of brain function and opens avenues for more sensitive biomarkers; e.g., in
early diagnosis of patients with mild cognitive impairment. He is also
interested in using real-time fMRI for neurofeedback applications.
Prof. Vincent Gripon
Time: 5:30 pm –
6:30 pm
Title: “Matching Convolutional Neural Networks with Graph Signals”
Abstract
Multi-layer perceptrons are on the paper a generalization of Convolutional
Neural Networks (CNNs). But on regular signals such as images or sounds, the
latter typically perform much better than the former. This is because CNNs take
into account priors about the signals: stationarity, compositionality and spatial
coherence. An increasing number of datasets contain signals for which those
priors are not explicitly well defined. Examples of such signals are: sensor
networks, point clouds, neuroimagery, social trends, citation networks. Proposing
methods that aim at leveraging the underlying structure in these settings has
thus become a very active field of research.
In this talk, we will overview the various methods proposed in the literature.
Some methods make use of the graph signal processing mathematical framework and
define operators in the spectral domain of the graphs that represent the
underlying structure of the signals. Some other methods directly tackle the
problem in the vertex domain and aim at extending convolution to signals on graphs.
We will mainly consider three problems: classification of signals on graphs,
classification of graphs and semi-supervised classification of vertices in a
graph.
CV
Vincent Gripon (S’10-M’12) received the MS degree from Ecole Normale Superieure
of Cachan and the PhD degree from Telecom Bretagne. He is a permanent
researcher with Telecom Bretagne (Institut Mines-Telecom), Brest, France. His
research interests lie at the intersection of information theory, computer
science, and neural networks. He is the co-creator and organizer of an online
programming contest named TaupIC which targets French top undergraduate
students. He is a member of the IEEE.
Organization:
Co-organized by EURASIP’s Special Area Team on Signal Processing for Multisensor
Systems (SPMus) and the Association for the Promotion of Science and Education,
a German nonprofit organization supporting children’s education.
EURASIP's Special Area Team on Signal Processing for Multisensor Systems (SPMus)