- PIA project ANSWER, involves Qwant and Inria, and is about creating the next-generation search engine. See more.
- ANR MAESTRO-5G (February 2019 – January 2022)
Neo is partner of the ANR project MAESTRO-5G: MAnagEment of Slices in The Radio access Of 5G networks. The project develops enablers for implementing and managing slices in the 5G radio access network, not only for the purpose of serving heterogeneous services, but also for dynamic sharing of infrastructure between operators. MAESTRO-5G develops a framework for resource allocation between slices and a business layer for multi-tenant slicing. It provides an orchestration framework based on Software Define Networking that manages resources and virtual functions for slices. A hardware demonstrator brings the slicing concept to reality and showcases the project’s innovations. The participants are: Orange Labs (coordinator), Nokia Bell Labs, Université d’Avignon et des Pays de Vaucluse, Inria (Projet-Teams Agora and Neo), Sorbonne Université, Telecom SudParis, CentraleSupélec.
- ANR Numerical Models MARMOTE (January 2013 – June 2017)
MAESTRO was partner and coordinator of the ANR project MARMOTE: MARkovian MOdeling Tools and Environments, ANR-12-MONU-0019. The MARMOTE project aimed at realizing the prototype of a software environment dedicated to modeling with Markov chains. It brought together seven partner teams, expert in markovian analysis, who developed advanced solution algorithms and applications in different scientific domains: reliability, distributed systems, biology, physics and economics. The participants were: University of Versailles Saint-Quentin (PRiSM), Telecom SudParis (SAMOVAR), University Paris-Est Créteil Val de Marne (LACL), University Pierre and Marie Curie (LIP6) and Inria (Project-Teams Diogen, Maestro, Mescal).
- MYDATA (Sept. 2018 – Nov. 2020) is a research project in cooperation with two other labs (LJAD and GREDEG) from Univ. Côte d’Azur to study how to achieve privacy through obfuscation. The project is funded by IDEX UCA^JEDI Academy 1 on “Networks, Information and Digital society.”
- Indo-French Exchange Grant Cefipra “Monte Carlo” (2015-2017)
This grant had K. Avrachenkov as principal investigator in MAESTRO, and involves IIT Bombay (PI V.S. Borkar) and IIS Bangalore (PI R. Sundaresan). Main topics of the project re stochastic numerical methods for network analytics.
- SticAmSud Project DyGaMe (January 2016 – December 2017)
Stochastic Dynamic Game Theory is developing in Engineering sciences and is in need of more theoretical results, algorithms and applications. The DyGaMe (Dynamic Games Methods: theory, algorithmics and applications) project brought together researchers from Applied Mathematics, Operations Research and Economics, with the objective of contributing to these aspects. It has more specifically concentrated on agent rationality and the game structure, looked for efficient solution algorithms by crossing Applied Mathematics and Operations Research techniques, and applied the results to problems originating from, on the one hand, security/conservation concerns, and on the other hand, sustainable development problems. Partners were MAESTRO, CNRS/LIP6 (Paris), CNRS-INRA/LAMETA (Montpellier), Universidad de Chile and Universidad Nacional de Rosario (Argentina).
Inria Associated Team: MALENA (Machine Learning for Network Analytics)
International Partner: Indian Institute of Technology Bombay (India) – Electrical Communication Engineering – Vivek Borkar
Duration: January 2017 – December 2019
In the past couple of decades network science has seen an explosive growth, enough to be identified as a discipline of its own, overlapping with engineering, physics, biology, economics and social sciences. Much effort has gone into modelling, performance measures, classification of emergent features and phenomena, etc, particularly in natural and social sciences. The algorithmic side, all important to engineers, has been recognised as a thrust area (e.g., two recent Nevanlinna Prize (J. Kleinberg 2006 and D. Spielman 2010) went to prominent researchers in the area of network analytics). Still, in our opinion the area is yet to mature and has a lot of uncharted territory. This is because networks provide a highly varied landscape, each flavour demanding different considerations (e.g., sparse vs dense graphs, Erdös-Rényi vs planted partition graphs, standard graphs vs hypergraphs, etc). Even adopting existing methodologies to these novel situations is often a nontrivial exercise, not to mention many problems that cry out for entirely new algorithmic paradigms. It is in this context that we proposed this project of developing algorithmic tools, drawing not only upon established as well as novel methodologies in machine learning and big data analytics, but going well beyond, e.g., into statistical physics tools.
Inria Associated Team: THANES (THeory and Application of NEtwork Science)
International Partner: Universidade Federal do Rio de Janeiro (Brazil) – Department of Computer and Systems Engineering – Daniel Ratton Figueiredo, Purdue University (USA) – Department of Computer Science – Bruno Ribeiro
Duration: January 2017 – December 2019
A first associated team with the same name THANES between Universidade Federal do Rio de Janeiro and Inria studied how services in Online Social Networks can be efficiently designed and managed, and spanned three years (January 2014 – December 2016). In the following associated team, the joint research activity continued along the line of network science with a focus on network growth models, community detection, information spreading, and recommendation systems for online social networks. A new research axis on deep learning spawned during 2018.