Associate Teams with Brazil
In addition to the Associate Teams with LNCC, these Associate Teams gathers Inria teams and other Brazilian partners
MoCoVec (Modelling and Biological Control of Vector-Borne Diseases: the case of Malaria and Dengue) is an Associate Team involving UNESP (Universidad Estadual Paulista, represented by Claudia Pio Ferreira) and the MAMBA Inria Project Team represented by Pierre-Alexandre Bliman. This project is cofounded by FAPESP and has started in 2020. It deals with modelling and biological control for vector-borne diseases, more specifically the case of malaria and dengue fever.
CACAO (Lower limb electrical stimulation for function restoration) is an Associate Team between the UnB (Universidade de Brasilia, Brazil) and CAMIN Inria Project Team. The UnB is represented by Emerson Fachin-Martins and Christine Azevedo Coste for Inria. The project, which was renewed in 2019,is cofounded by FAPESP. It aims at proposing approaches and solutions that can have an effect on people who suffered a partial or total paralysis improving their quality of life by electric simulation (ES). It induces muscle contraction substituting or completing the normal volitional control. When applied to function restoration (motor or sensory), the technique is usually called Functional Electrical Stimulation (FES).
CAPOEIRA (Computational approaches with the objective to explore intra and cross-species interactions and their role in all domains of life) is an Associate Team between the Inria European Project Team ERABLE and USP (Universidade de Sao Paulo, Brazil). It is coordinated by André Fujita from USP and Marie-France Sagot from ERABLE- Inria. The project started on January 1st, 2020 for a duration of three years. It is cofunded by FAPESP. The project covers theoretical computer science (essentially graph theory), mathematics (combinatorics, statistics, and probability), and the development of algorithms to address various biological questions, in particular, the intra and cross-species interactions, which have implications in all aspects of life sciences, including health, ecology, and environment.
LOGIC (Learning on graph-based hierarchical methods for image and multimedia data) is an Associate Team gather PUC MG (Pontifícia Universidade Católica de Minas Gerais) and the LINKMEDIA Inria Project Team. It involves Silvio Jamil Guimaraes for the Brazilian side and Simon Malinowski for the French one. The project has started in 2020 and focuses on learning on graph-based hierarchical methods for image and multimedia data.
KEPLER (Probabilistic foundations for time, a key concept for the certification of cyber-physical systems) is an Associate Team between UFBA (Universidade Federal de Bahia, Brazil) and the COPERNIC Inria Project Team which started in 2020. It is led by George Lima for the Brazilian side and Liliana Cucu for the French one.
The project addresses the topic of probabilistic foundations for time as a key concept for the certification of cyber-physical systems.
MOUSTIQ (Modelization and control of infectious diseases, wave propagation in heterogeneous media and nonlinear dispersives equations) is an Associate Team between UFPB (Universidade Federal de Paraiba, Brazil) and the SPHINX Inria Project Team. The Brazilian side is represented by Felipe Chaves and the French one by Ludovick Gagnon. It has started in 2020. The project addresses modelization and control of infectious diseases, wave propagation in heterogeneous media and nonlinear dispersive equations.
ReDaS (Analysis Techniques and Workflow Methodologies for Reproducible Data Science) is an Associated Team, proposed by the POLARIS Inria research team and conducted together with researchers from UFRGS (Universidade Federal do Rio Grande do Sul, Brazil). The project will be conducted between 2019 and 2021 by Guillaume Huard for the French side and Lucas Mello Schnorr for the Brazilian one. The main scientific goal of this project is to develop novel analysis techniques and workflow methodologies to support reproducible data science.
DrIVE (Distributed Intelligent Vehicular Environment) is an Associate Team between Unicamp (Universidade de Campinas, Brazil) and the DIANA Inria Team Project in France. University of Santa Cruz (California, USA) is also involved as part of Inria@SiliconValley. It is headed by Christian Esteve Rothenberg and Mateus Augusto Silva Santos representing Unicamp and Thierry Turletti representing Inria. It started in 2018 and is cofounded by the FAPESP. As such, the main objectives of the DrIVE associated team are to: develop a programmable network control plane that will dynamically adjust to current environment conditions and network characteristics to support ITS’ scalability, quality of service (QoS), and decentralization requirements, and apply the proposed distributed network control plane framework to ITS services and applications, such as road hazard warning, autonomous- and self-driving vehicles, and passenger-centric services (e.g., infotainment and video streaming).
THANES (THeory and Application of NEtwork Science) is a joint Brazilian-French research team. It was created in March 2014 and is financially supported for 3 years by Inria (the French institute for research on computer science) and FAPERJ (the Research Support Foundation of the State of Rio de Janeiro).
The team investigates network science problems with a particular focus on Online Social Networks (more on our research here). It counts 11 members: 6 from the Inria project-team Maestro, 4 from UFRJ (Universidade Federal do Rio de Janeiro)and 1 from Carnegie Mellon University.
EMBRACE (Everaging human Behavior Resource AlloCation modEls) is the name of the Inria Associate Team between the INFINE Inria Project Team and three Brazilian Universities: the main Brazilian Team was from UFMG (Universidade Federal de Minas Gerais), and the satellite Teams were from UFG (Universidade Federal de Goias) and UTFPR (Universidade Tecnologica Federal do Paraná). The project was led by Aline Carneiro Viana for the French side and by Antonio A. Ferreira Loureiro for the Brazilian one from 2015 to 2019. It addresses the topic of designing efficient solutions for 5G networks considering human behavior, uncertainty, and heterogeneity of networking resources.