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Ph.D de

Ph.D
Group : Parallelism

"Algorithmique probabiliste pour systèmes distribués émergents"

Starts on 01/10/2006
Advisor : ROZOY, Brigitte

Funding : COLL TERR
Affiliation : Université Paris-Saclay
Laboratory : LRI

Defended on 19/10/2009, committee :
Hugues Fauconnier, maître de conférences à l'université PARIS 7
(rapporteur)

Laurent Fribourg, directeur de recherche à l'ENS Cachan (examinateur)

Colette Johnen, professeur à l'université Bordeaux 1 (rapporteuse)

Shay Kutten, professor at The William Davidson Faculty of Industrial
Engineering and Management (examinateur)

Stéphane Messika, maitre de conférences à l'université Paris 11 (directeur
de thèse)

Michel Raynal, professeur à l'université Rennes 1 (rapporteur)

Brigitte Rozoy , professeur l'université Paris 11 (directrice de thèse)

Research activities :

Abstract :
Mobile sensor networks have appeared in computer science several years
ago. Some of these networks’ characteristics are new: sensors are small,
with few memory; they can be corrupted easily and are mobile. Moreover,
they may contain thousands of entities.
For computer science, the stake is huge. All these new properties are a
challenge for us, algorithm creators. It is necessary to adapt our methods
and to make sure that, from an algorithmic point of view, these new
systems
will function correctly in the years to come. The theoretical difficulty
and the stake of these issues transform them into an interesting and
exciting research subject.
The goal of this thesis is to reconsider some of the algorithms created
for classical networks, in order to make them performing on these new
networks. We did not restrain ourselves to mobile sensor networks and also
considered other recent systems. Also, we always introduced probability,
in order to unblock impossibilities or to improve the performance of the
algorithms. We obtained different results on several kinds of new networks
as peer to peer networks, robots networks or mobile sensor networks in
which we extend the population protocol model.
Finally, we introduced a formal model in order to prove that at some level
of abstraction, there are very strong connections between the various
types of networks or, at least, between the models describing them.

Ph.D. dissertations & Faculty habilitations
MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.

A NEW GENERATION OF GRAPH NEURAL NETWORKS TO TACKLE AMORPHOUS MATERIALS


SPOTTING NEURAL NETWORK BOTTLENECKS AND FIXING THEM BY ARCHITECTURE GROWTH