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March 2018

Eindhoven Stochastic Seminar

Mar 28, 15:45 - 16:45

Liudmila Prokhorenkova (Yandex) Community detection through likelihood optimization: in search of a sound model Community detection has recently became one of the most important problems in network analysis and many algorithms have been proposed to solve this problem. Among others, methods based on statistical inference are of particular interest: such algorithms are mathematically sound and were shown to provide partitions of good quality. The main ingredient of statistical inference methods is to assume some underlying random graph model (a.k.a. "null…

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April 2018

“Mathematical Techniques in Optical Networks”: Introduction and Scope

Apr 16 - Apr 18

Sponsors Summary Optical networks are a key part of current day technological progress. They form the backbone of all future communication networks like 5G, internet of things and such. Optical Networks form a very important field of research in engineering, and also pose a lot of interesting mathematical problems. The workshop Mathematical Techniques in Optical Networks is conducted under the auspices of NETWORKS and The Research Center for Integrated Nanophotonics  at Eurandom. The goal of the workshop is to bring together the…

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May 2018

Franco-Dutch YEP Workshop

May 28 - Jun 1
Eurandom, Metaforum
Eindhoven, Netherlands
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"Mathematical statistical mechanics, random graphs and related topics" Sponsored by    Aléa-Networks-Eurandom fellowships for the Franco-Dutch YEP To apply for support for travel/accommodation, please see: GdRI CNRS AléaNetworks   Summary The model of the workshop will be the Young European Probalilists series (YEP) that are held annually in Eurandom, consisting of mini-courses and regular talks from both "junior" or "senior" researchers from the Dutch and French probabililist communities. Organizers The meeting is co-organized within the stochastic groups of TU Eindhoven and…

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June 2018

CCEGN3 “Critical and Collective Effects in Graphs and Networks”

Jun 18 - Jun 22

Critical and Collective Effects in Graphs and Networks

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