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Leader: Valerie VIET TRIEM TONG (CentraleSupélec)

Confidentiality, Integrity, Availability, Repartition

Scientific project

(Page site web en anglais)

Research domains

(Texte en anglais)

In the field of security and distributed systems, the CIDRE team focuses mainly on the three following topics:

  • Intrusion Detection;
  • Privacy Protection;
  • Trust Management.

Have a look on:

Créée le : 01/07/2011
Établissement de rattachement : Université de Rennes 1, Inria, CNRS, CentraleSupélec
Localisation : Rennes et Cesson-Sévigné (35)



Leader: Gildas AVOINE & Pierre-Alain FOUQUE

'EMbedded SEcurity and Cryptography'

The research team "Embedded Security and Cryptography" (EMSEC) addresses questions related to computation security and to electronic ubiquitous systems. EMSEC especially focuses on the application of cryptography in the security of data, hardware (e.g., smartcards, RFID, FPGAs), and software implementations (on smartphones, embedded systems, etc.). The research activities of this team target both the construction of security-preserving mechanisms and the design of new attacks, and addressing research questions with a four-layer approach. These are, from the most theoretical to the most practical:

  1. (1) Security models and proofs.
  2. (2) Design and attacks of building blocks to secure communicating objects (cryptography on elliptic curves, authentication protocols with distance bounding, authenticated encryption, cryptanalytic time-memory trade-off, etc.).
  3. (3) Attacks on implementations (on smartcards, RFID tags, smartphones, side-channel attacks, forensics and reverse-engineering), and countermeasures.
  4. (4) Cryptanalysis of real-life embedded systems.
Created since: 23 Sep. 2014 Team : 5 Feb. 2016
Associated establishments: INSA Rennes, Université de Rennes 1, CNRS
Location: Campus of Beaulieu, Rennes


Leader: Gabriel ANTONIU (Inria)

Texte en anglais

Our research activities address the area of distributed data management at challenging scales, with a particular focus on clouds and petascale HPC architectures. We target data-oriented high-performance applications that exhibit the need to handle massive non structured data – BLOBs: binary large objects (on the order of terabytes) – stored in a large number (thousands to tens of thousands), accessed under heavy concurrency by a large number of clients (thousands to tens of thousands at a time) with a relatively fine access grain (on the order of megabytes). Examples of such applications are:

  • Cloud data-mining applications (e.g., based on the MapReduce paradigm) handling massive data distributed at a large scale
  • Advanced (e.g., concurrency-optimized, versioning-oriented) cloud services both for user-level data storage and for virtual machine image storage and management at IaaS level
  • Distributed storage for post-Petascale computing applications
  • Storage for desktop grid applications with high write throughput requirements.

Our approach strongly relies on experimentation on the Grid’5000 platform.

Created since: Pré-équipe 15/05/12; Team: 1/07/12
Associated establishments: University of Rennes 1, Inria, INSA Rennes, ENS Rennes
Location: Campus of Beaulieu, RENNES (35)


Leader: Guillaume PIERRE (Université de Rennes 1)

The MYRIADS team gathers researchers in large scale distributed systems. The long-term goal of the MYRIADS project-team is to build next generation utility computing platforms by designing and implementing systems and environments for autonomous service and resource management in large virtualized infrastructures. This requires tackling the challenges of dependable application execution and efficient resource management.

Créée le : Équipe 01/01/12
Établissement de rattachement : Inria, Université de Rennes 1, CNRS, INSA Rennes
Localisation : Campus de Beaulieu, Rennes (35)



Leader: François TAIANI (Université Rennes 1)

The World Is DistributEd, Exploring the tension between scale and coordination

As distributed computing systems are being deployed within a growing number of everyday applications, they are reaching unheard-of levels of scale, dynamicity, and complexity. The construction of such systems requires novel fundamental approaches that stand in stark contrast to existing strategies in many areas of distributed computing research. The objective of the WIDE team is to provide such a foundation, by investigating some of the key fundamental theoretical and practical questions posed by modern distributed computer systems.

More specifically, we would like to explore the inherent tension between scalability and coordination guarantees, and develop novel techniques and paradigms that are adapted to the rapid and profound changes impacting today's distributed systems, both in terms of the application domains they support and the operational constraints they must meet.

Our research revolves around four key objectives.

  • Objective 1: Designing Hybrid Scalable Architectures,
  • Objective 2: Constructing Personalizable Privacy-aware distributed systems,
  • Objective 3: Understanding Controllable Network Diffusion Processes,
  • Objective 4: Systemizing Modular Distributed Computability and Efficiency.

These four objectives have in common the inherent tension between coordination and scalability in large-scale distributed systems: strong coordination mechanisms can deliver strong guarantees (in terms of consistency, agreement, fault-tolerance, and privacy protection), but are generally extremely costly and inherently non-scalable if applied indiscriminately. By contrast, highly scalable coordination approaches (such as epidemic protocols, eventual consistency, or self-organizing overlays) perform much better when the size of a system increases, but do not, in most cases, provide any strong guarantees in terms of consistency or agreement.

The above four objectives explore these tensions from four complementary angles: from an architectural perspective (Objective 1), from the point of view of a fundamental system-wide guarantee (privacy protection, Objective 2), looking at one universal scalable mechanism (network diffusion, Objective 3), and considering the interplay between modularity and computability in large-scale systems (Objective 4). These four objectives range from practical concerns (Objectives 1 and 2) to more theoretical questions (Objectives 3 and 4), yet present strong synergies and fertile interaction points. E.g. better understanding network diffusion (Objective 3) is a key enabler to develop more private decentralized systems (Objective 2), while the development of a theoretically sound modular computability hierarchy (Objective 4) will have a direct impact on our work on hybrid architectures (Objective 1).

Fact following teams: ASAP
Created since : 01 janvier 2018
Associated establishments: Inria, Université de Rennes 1
Localisation : Campus of Beaulieu, Rennes



Leader: Nicolas MONTAVONT

Have a look on french page

Created since: 01/01/12
Associated establishments: Institut Mines Telecom - IMT Atlantique, CNRS
Location : Campus of Cesson-Sévigné (35)



Leader: Xavier LAGRANGE (Pr. IMTAtlantique)

ADOPNET (Advanced Technologies for Operated Networks) contributes to the specification of architectures, protocols, control mechanisms, and monitoring mechanisms for the next generation networks. Our goal is to build networks that are flexible, adaptive, energy-efficient, secure, and able to deliver content on a large scale to various types of terminals. ADOPNET, in particular, addresses the convergence of access networks, the combination of radio and optical technologies, and adaptive software-based content delivery networks. ADOPNET research team succeeds to ATNET and REOP teams.

Created since: 29 mai 2015
Associated establishments: Université Rennes 1, Institut Mines Telecom - IMT Atlantique
Location : Campus of Brest Iroise / Rennes – Campus of Beaulieu / Cesson-Sévigné


Leader: Frédéric GUIDEC (Université Bretagne Sud)

CASA : The team focuses on supporting communication and services in partially or intermittently connected networks. It notably investigates the DTN (Delay/Disruption Tolerant Networking) approach as a means to reach these goals.


The research activity of team CASA aims at supporting communication and service provision in challenged environments, and most notably in partially or intermittently connected mobile networks. The team mostly focus on the Opportunistic Networking model, and investigate how this model can help support communication and services in challenged networks.

Part of team CASA activity consists in designing opportunistic routing protocols, and implementing these protocols in communication middleware so they can be tested in real conditions.

They also investigate how distributed applications can be designed and implemented for networks whose characteristics keep changing spontaneously and unpredictably. The term Opportunistic Computing has been introduced recently in the literature in order to refer to a new computing paradigm that relies exclusively on pairwise contacts bewteen mobile hosts. Team CASA strives to contribute to the development of this computing paradigm by designing methods, models, and middleware tools that make it easier for programmers to develop distributed applications for opportunistic networks.


Delay/Disruption-Tolerant Networking (DTN); Opportunistic Networking/Computing; Mobile Ad Hoc Networks (MANETs); Wireless Sensor Networks (WSNs); Service-Oriented Architecture.

Created since: 01/07/07
Associated establishments: Université Bretagne Sud (UBS)
Location: Vannes (56) Campus of UBS



Leader: Gerardo RUBINO

Consult the french page.


Leader: Jean-Marie BONNIN (IMT Atlantique)

Enabling Affordable Smarter Environment

The EASE proposal aims to help pervasive application designers in the development phase as well as to ease the life cycle management. We will develop a comprehensive set of new interaction models and system architectures following three main principles:

  • We would be able to enrich and to manage locally data produced in the environment. By proposing data-centric service architectures, the developer must be able to identify simply the data he/she wants to collect. In return the application would then be able to build their knowledge about their environment (perception) in order to adjust their behavior (eg. level of automation) to the actual situation.
  • Pervasive applications should be able to describe requirements they have on the quality of their environment perception. We would be able to achieve the minimum quality level adapting the diversity of the sources (data fusion/aggregation), the network mechanisms used to collect the data network/link level) and the production of the raw data (sensors).
  • Rebuilding a complete virtual mirror of the environment is often required, and out-off-silo designs generally relies on cloud-based approaches. We want to leverage local properties and direct interactions between objects to propose new service architectures to avoid the need for such a reconstruction of the reality. Concretely, the research objectives proposed for the EASE project are divided into three axes divided in subaxes. For each sub-axe, we present the research objective and the expected results.

Axis 1  - Collecting pertinent information

  • Data characterization
  • Data fusion
  • Assessing the correctness of the behavior

Axis 2 - Building relevant abstraction for new interactions

  • Tagging the environment
  • Taking advantages of the spatial and temporal relationships

Axis 3 - Acting on the environment


Fact following the teamTACOMA
Created since: : 09 November 2018
Associated establishments: IMT Atlantique de Rennes,  Inria, Université de Rennes 1 
Location: Campus de Beaulieu à Rennes, et locaux IMT Atlantique à Cesson-Sévigné


Department: D3 - ARCHITECTURE


Leader: Olivier SENTIEYS

The Cairn project-team researches new architectures, algorithms and design methods for flexible and energy efficiency domain-specific system-on-chip (SoC).

As performance and energy-efficiency requirements of SoCs are continuously increasing, they become difficult to fulfil using only programmable processors solutions. To address this issue, we promote/advocate the use of reconfigurable hardware, i.e. hardware structures whose organization may change before or even during execution. Such reconfig- urable SoCs offer high performance at a low energy cost, while preserving a high level of flexibility. The group studies these SoCs from three angles: (i) The invention and design of new reconfigurable platforms with an emphasis on flexible arithmetic operator design, dynamic reconfiguration management and low- power consumption. (ii) The development of their corresponding design flows (compilation and synthesis tools) to enable their automatic design from high-level specifications. (iii) The interaction between algo- rithms and architectures especially for our main application domains (wireless communications, wireless sensor networks and digital security). The team has been created on January the 1st, 2008 and is a “reconfiguration” of the former R2D2 research team from Irisa.

The development of complex applications is traditionally split in three stages: a theoretical study of the algorithms, an analysis of the target architecture and the implementation. When facing new emerging applications such as high-performance, low-power and low-cost mobile communication systems or smart sensor-based systems, it is mandatory to strengthen the design flow by a joint study of both algorithmic and architectural issues. The figure below shows the global design flow that we propose to develop. This flow is organized in levels which refer to our three research themes: application optimization (new algorithms, fixed-point arithmetic and advanced representations of numbers), architecture optimization (reconfigurable and specialized hardware, application-specific processors), and stepwise refinement and code generation (code transformations, hardware synthesis, compilation).

Keywords: Hardware Accelerators, Compiling, Embedded Systems, Energy Consumption, Parallelism, Wireless Sensor Networks, Security, Signal Processing, Reconfigurable Hardware, Computer Arithmetic, System-On-Chip

Created since: 01/01/2009
Associated establishments: Université de Rennes 1, Inria, CNRS, ENS Rennes
Location: Rennes (35) and Lannion (22)


Leader: Olivier BERDER

Even the smallest sensors are now able to send their data over what is called Internet of Things (IoT), such that every user in the world could reach it. But the more sensors we place, the less we want to change batteries! In such a context, the GRANIT team purpose is to design algorithms and architectures able to adapt to environment parameters, such as propagation channel characteristics, wireless traffic conditions or network topology while respecting applications requirements in terms of data rate, reliability, latency, and most of all, life time of involved systems.

Created since: 05/01/15 Team : 04/12/15
Associated establishments: Université de Rennes 1, CNRS
Location : Campus de Lannion (22) – University of Rennes 1


Leader: Erven ROHOU

Pushing Architecture and Compilation for Application Performance

The general research direction is the performance of computing systems. Building upon the expertise of its members, Pacap will develop techniques based on compilation, manipulation of executables in binary form, and microarchitecture. Transversal aspects of interest to Pacap include reliability, security, green computing.

Fact following teams:  ALF
Created since: 16/09/2016
Associated establishments: Inria,Université de Rennes 1
Location : Campus of Beaulieu, RENNES (35)




Leader: Flavio OQUENDO (Univ. Bretagne Sud)

The main research domain of the ArchWare team concerns the software architecture. The software architecture provides the abstraction in order to rigorously design, develop and evolve software-intensive systems. It is the cornerstone to tame system complexity and to satisfy extra-functional requirements. The team develops innovative and sound languages, processes, and tools for architecting evolving software. The main project of team focuses on the scientific and technological challenges raised by architecting Systems-of-Systems (SoS).

Research directions

Archware addresses the scientific and technological challenges raised by the software architecture of complex software-intensive systems. In particular, it addresses an emerging class of evolving software-intensive systems that is increasingly shaping the future of our software-reliant world, the so-called System-of-Systems (SoS). SoSs exhibit evolutionary architectures for creating emergent behavior to meet global missions.

The targeted breakthrough for Archware is to conceive sound foundations and a novel holistic approach for architecting trustworthy software-intensive SoSs, encompassing:

  • Abstractions, formalisms and underlying computational models to formally describe and analyze the software architecture of SoSs;
  • Abstractions, formalisms and mechanisms to construct, manage, and evolve SoSs driven by architecture descriptions, while resiliently enforcing their correctness, effectiveness, and efficiency;
  • Abstractions, formalisms and mechanisms for specifying and operating SoS missions, deriving abstract architectures, as well as generating concrete SoS architectures in operational environments;
  • Abstractions, formalisms and mechanisms for co-specifying and co-enforcing cybersafety and cybersecurity to achieve trustworthiness in SoS architectures.
Created since: 1st januar 2012
Associated establishments: Universitty of South Britanny (UBS)
Location: Vannes (56)



Leader: Thomas JENSEN

The goal of the Celtique project is to improve the security and reliability of software through software certificates that attest to the well-behavedness of a given software. We aim at providing certificates issued from semantic software analysis. The semantic analyses extract approximate but correct descriptions of software behaviour from which a proof of security can be constructed.

The analyses of relevance include numerical data flow analysis, control flow analysis for higher-order languages, alias and points-to analysis for heap structure manipulation and data race freedom of multi-threaded code.

Team created since: 1st july 2009
Associated establishments: Université de Rennes 1, Inria, INSA Rennes, ENS Rennes
Location: Rennes (35)



Leader: Olivier BARAIS (Université Rennes 1)

DIVERsity-centric Software Engineering

Our main objective is to automatically compose and synthesize software diversity from design to runtime to address unpredictable evolutions of software intensive systems. We address this objective through 4 main research axis: software language engineering, software variability, software adaptation and software diversification.

Research axis

The research in DiverSE is organized around 4 research axis:

  • Software Language Engineering to handle the diversity of languages used by the stakeholders involved in the construction of software intensive systems
  • Software Product Lines to address the diversity of features required by the different customers of these systems
  • Distributed architecture and deployment to handle the diversity of runtime environments in which software has to run and adapt
  • Software diversity and testing to enhance the resilience of software.

These four axis share and leverage the scientific and technological results developed in the area of model-driven engineering in the last decade. This means that all our research activities are founded on sound abstractions to reason about specific aspects of software systems, compose different perspectives and automatically generate parts of the system.

Fact following the team: TRISKELL
Created since: 01/07/2014
Associated establishments: University of Rennes 1, Inria, INSA Rennes
Location: Rennes (35)


Leader: Benoît CAILLAUD

Modélisation hybride et conception par contrats pour les systèmes embarqués multi-physiques

The Hycomes team focuses on cyberphysical systems design, combining physics and software. The applications are related to the design and optimal exploitation of industrial systems such as transport (aircrafts, railway) or energy systems. Two research tracks are developed:

  1. Hybrid systems modeling, where continuous-/discrete-time dynamics are combined;
  2. Contract-based design methods for cyberphysical systems.

More precisely, the Hycomes team is addressing the following problems:

  • The design of hybrid system modeling languages, based on differential algebraic equations, enabling a modeling style close to the way physicists model devices in various domains (multibody mechanics, hydraulics, electronics, thermal);
  • Faithful simulation techniques, supporting a rigorous semantics;
  • Modular compilation techniques for hybrid systems languages, with the objective of improving the reusability of components models.
  • Contract-based reasoning techniques, supporting the requirements engineering stages, appearing early in the design process of cyberphysical systems.
Team created since: 01/07/13
Associated establishments: Inria, CNRS
 Location: campus de Beaulieu, RENNES (35)


Leader: Sophie PINCHINAT

Logic and Applications


Nowadays, many of our daily activities which were in the past performed in the ‘real’ world and in interaction with other humans, are carried out in a digital world in interaction with non-human ‘agents’: classic examples are e-commerce, e-voting, e-banking, e-government, etc. . . This transposition of some of our activities into the digital world already plays an important role in our everyday life. This transposition is expected to develop in the future, which is certainly desirable in order to harmonize the rate at which our society evolves. This large picture exhibits an urgent need for both taming already existing e-activities and assisting the birth of new ones.

Existing e-activities, such as e-voting, e-commerce, e-banking, e-government etc. rely on a combination of numerous technologies either at the physical/hardware level or at the digital/software one. The nature of interaction between different services that form the whole application is very complex and leads to critical issues regarding its quality that the research community together with industry try to resolve. Among the main issues, we can mention privacy, legal process, correction of the functionalities. Also, the growing development of applications to support e-activites urges the designers to elaborate methodologies that would allow them to exploit adaptability or re-usability of existing services.

Whichever issue can be picked, rigorous settings are required in order to make evidence of the correctness, the quality, the robustness, etc. of the existing products.

Moreover, some sectors of activity currently suffer from a lack of connection with the digital world: typically, legal processes are very far from being computerized or even computer-assisted, nor are our abilities to remote control some domestic processes such as closing roller blinds when a storm is forecast, and so on. We believe that the afore-mentionned rigorous settings should help in designing new e-activities that support underdeveloped domains currently operated by hand.

The Logica Project lies in this will to bring out the capabilities to rigorously analyze or design the functionalities of services in e-activities, with a focus on interaction issues from a logical perspective.

Research directions

The project gathers experts in logic who aim at contributing in the development of logical theories to provide a solid framework to analyze and design applications for e-activities. We propose three main actions which contribute to this aim:

  • Logics and Interaction
  • Practical applications
  • Disseminating logic
Created since : Team 03/07/15
Associated establishments: University of Rennes 1, ENS Rennes
Location : Campus of Beaulieu, RENNES (35)



Leader: Nathalie BERTRAND (Inria)

SUpervision of large MOdular and distributed systems

The SUMO team proposes to combine formal methods approaches with concurrency theory, in order to address the modeling, analysis and management of large distributed or modular systems exhibiting quantitative aspects. Large distributed softwares and systems are indeed calling for quantitative models involving time, probabilities, costs, and combinations of them. As many problems in this setting become untractable or even undecidable, we are interested in the design of efficient approximation techniques, for example borrowed from electrical engineering approaches to the management of large stochastic systems. A strong point of SUMO is to gather skills from formal methods, discrete event systems, concurrency theory, and electrical engineering. Several application fields are covered: telecommunication networks management, modeling and verification of web services, control issues in large data centers, plus more opportunistic applications in the field of embedded systems or biological pathways.

Team created since: 01/01/13
Associated establishments: Inria, Université de Rennes 1, CNRS
Location: Campus of Beaulieu, RENNES (35)



Leader: Olivier ZENDRA

Threat Analysis and Mitigation for Information Security

Tamis is a new secrutiy team at Inria Rennes. The team studies new attacks (offensive security) on complex systems and technologies. It also study a panoply of test-based and formal methods approaches to detect vulnerability and malwares.

The team implements the research done within the High Security Laboratory (LHS) leaded by Jean-Louis Lanet.

Fact following teams: ESTASYS
Created since:: 01/01/16
Associated establishments:  Inria, CNRS

Location : Campus of Beaulieu, RENNES



Leader: Jean-Pierre TALPIN
Tim, Events and Architectures

Time modeling in system design

    •  Time systems and calculi — logical and algebraic representations
    •  Time abstractions and refinements — logical and algebraic relations among time domains
    •  Conformance and mitigation — Verification of timed quantitative properties, automated synthesis of adapters for synchronisation

Time as a viewpoint in system analysis

    •  Logic and quantitative reasoning for analysis and verification
    •  Type inference, abstract interpretation, SAT/SMT verification
    •  Control and schedule synthesis, abstract affine scheduling
    •  Types, modules, interface and contract algebra

Application to embedded system design

    •  An infrastructure for polychronous modeling, analysis and (translation validated) code generation, the Eclipse IWG Polarsys project Polychrony on Polarsys
    •  A standard for modeling time  in architecture analysis and design
    •  Architecture exploration, virtual prototyping, virtual integration

Fact following the teamESPRESSO
Created since: 01/01/14
Associated establishments: Inria, CNRS
Location: Campus of Beaulieu, RENNES



Leader: Christine GUILLEMOT

Efficient processing, i.e. analysis, storage, access and transmission  of visual content, with continuously increasing  data rates, in environments which are more and more mobile and distributed, remains a  key challenge of the years to come. The emergence of new image modalities leads to a sustained need for algorithmic tools allowing  efficient compression and communication of large volumes of visual data, of visual features and descriptors extracted for different processing tasks.

Building upon a strong background on signal/image/video processing and  information theory, the goal of the project-team is the design of algorithms and practical solutions in the areas of visual data analysis, modeling, representation, compression and communication. More precisely, the team tackles different theoretical and practical issues of  the visual data analysis, compression, communication and rendering chain, which are complementary and can hardly be addressed separately in networked application scenarios.  Our activities are thus structured around the following  inter-dependent axes:

  • Analysis and modeling for compact representation and navigation in large volumes of visual data
  • Representation and compression of visual data
  • Distributed processing and robust communication of visual data

The proposed research is at the frontier of computer vision, signal processing, coding and information theory. In terms of application  domains, the project will primarily target networked visual  applications such as 3DTV, FTV, camera sensor networks, and medical  imaging applications. However, tools developed for video analysis will find natural applications in visual content editing as well as retrieval, in particular in the context of band-limited networked applications (e.g. mobile retrieval scenarios).

Team created since: 01/01/12
Associated establishments: Inria, Université de Rennes 1, CNRS
Location : Campus of Beaulieu, RENNES (35)



Leader: Pierre MAUREL (UR1)

Team presentation

Empenn (means "Brain" in Breton language) research team is jointly affiliated with Inria, Inserm (National Institute of Health and Scientific Research), CNRS (INS2I institute), and University of Rennes I. It is a team of IRISA/UMR CNRS 6074. Empenn is based in Rennes, at both the medical and science campuses. The team follows the "VisAGeS” one that was created for 12 years in 2006 by Inria, As for "VisAGeS”, Empenn hosts the accreditation number U1228 renewed by Inserm in 2017, after a competitive evaluation conducted by both HCERES and Inserm.

Through this unique partnership, the ambition of Empenn is to establish a multidisciplinary team bringing together researchers in information sciences and medicine. Our medium- and long-term objective is to introduce our basic research to clinical practice, while maintaining the excellence of our methodological research.

Our goal is to foster research in medical imaging, neuroinformatics and population cohorts. In particular, the Empenn team  targets the detection and development of imaging biomarkers for brain diseases and focus its efforts on translating this research to clinics and clinical neurosciences at large.

In particular, the objective of Empenn is to propose new statistical and computing methods, and to measure and model brain morphological, structural and functional states in order to better diagnose, monitor and deliver treatment for mental, neurological and substance use disorders. We propose combining advanced instrumental devices and new computational models to provide advanced diagnosis, therapeutic and neuro-rehabilitation solutions for some of the major disorders of the developing and aging brain.

Generic and challenging research topics in this broad domain include finding new ways to compare models and data, assist decisions and interpretation, and develop feedback from experiments. These activities are performed in close collaboration with the Neurinfo in vivo imaging platform, which is a critical environment for the experimental implementation of our research on challenging clinical research projects and the development of new clinical applications.

Research themes

New practices in medicine bring new challenges in information sciences. This is acutely challenging for brain disorders,where the main challenges we facetoday include (1) improving the understanding of the brain (especially the brain in action), (2) undertaking more effective monitoring of therapeutic procedures, (3) modeling groups of normal and pathological individuals from signal and image descriptors, and (4) discovering new therapeutic and rehabilitation strategies for brain recovery. In addressing these challenges, current medicine lacks computational models able to align multimodal and multiscale observations ofpatients withunderlying pathological phenomena,as well as frameworks to validate these models in clinical settings. These issues pose new challenges in the field of digital sciences and require the development of new solutions for (1) mining descriptors from in vivo observations,(2) assimilating the large amount of data produced for each patient through compact and relevant mathematical representations, (3) learning the dynamics of spatiotemporal data to predict the course of the disease in individual patients, and (4) reconciling observations and treatment processes (the theragnostics concept).

In this context, some of our major developments and newly arising issues and challenges include:

  • The generation of new descriptors to study brain structure and function (e.g. the combination of variations in brain perfusion with and without a contrast agent; changes in brain structure in relation to normal, pathological, functional or connectivity patterns; or the modeling of brain state during cognitive stimulation using neurofeedback).
  • The integration of additional spatiotemporal and hybrid imaging sequences covering a larger range of observations, from the molecular level to the organ one, via the cellular level (arterial spin labeling, diffusion MRI, MR relaxometry, MR fingerprinting, MR cell labeling imaging, MR-PET molecular imaging, EEG-MRI-NIRS functional imaging, etc.).
  • The creation of computational models through the data fusion of molecular, cellular (i.e. through dedicated ligands or nanocarriers), structural and functional image descriptors from group studies of normal and/or pathological subjects.
  • The evaluation of these models in relation to acute pathologies, especially for the study of degenerative, psychiatric, traumatic or developmental brain diseases (primarily multiple sclerosis, stroke, traumatic brain injury (TBI) and depression, but applicable with a potential additional impact to epilepsy, Parkinson’s disease, dementia, PTSD, …) within a translational framework.

In terms of new major methodological challenges, we will address the development of models and algorithms to reconstruct, analyze and transform the images, and to manage the mass of data to store, distribute and “semanticize” (i.e. provide a logical division of the model’s components according to their meaning). As such, we expect to make methodological contributions in the fields of model inference; statistical analysis and modeling; the application of sparse representation (compressed sensing and dictionary learning) and machine learning (supervised/unsupervised classification and discrete model learning); data fusion (multimodal integration, registration, patch analysis, etc.); high-dimensional optimization; data integration; and brain-computer interfaces.

In summary, we expect to address the following major challenges:

  • Developing new information processing methods able to detect imaging biomarkers in the context of mental, neurological, and substance use disorders.
  • Providing new computational solutions, allowing a more appropriate representation of data for image analysis and the detection of biomarkers specific to a form or grade of pathology, or specific to a population of subjects.
  • Providing new patient-adapted connectivity atlases for the study and characterization of diseases from quantitative MRI.
  • Providing new analytical models of dynamic regional perfusion, and deriving indices of dynamic brain local perfusion from normal and pathological populations.
  • Investigating whether the theragnostics paradigm of rehabilitation from hybrid neurofeedback can be effective in some behavioral and disability pathologies.

These major advances are primarily developed and validated in the context of several priority applications: multiple sclerosis, stroke rehabilitation, and the study and treatment of depression

In terms of scientific organization, our research project are organized around three major technological and basics cience topics (Population Imaging, Detection and Learning,and Quantitative Imaging) and three major translational axes (Behavior, Neuro-inflammationand Recovery) that are generic enough to address a large range of central nervous system diseases.

Created since: 01/01/2019
Associated establishments: CNRS, Université de RENNES 1, INSERM, Inria
Location : RENNES Campus of Beaulieu and Campus of Villejean



Leader: Sébastien LEFEVRE

The overall objective of the team is the processing of complex images for environmental purposes. In such a context, available data form a massive amount of multidimensional (multi- or hyperspectral) noisy observations with high spatio-temporal variability. While understanding these data stays very challenging, environmental systems always come with some additional knowledge or models which are worth being exploited to achieve environment observation. Finally, whatever the task involved (e.g., analysis, filtering, classification, clustering, mining, modelling, …), specific attention has to be paid to the way results are provided to the end-user, helping them to benefit from their added value.

Created since: 29/05/15
Associated establishments: CNRS, Université Bretagne Sud
Localisation : Campus of Vannes - UBS



Leader: Frédéric BIMBOT


Parsimony and New Algorithms for Audio & Signal Modeling

Building upon the rare scientific culture of the former METISS project-team, at the interface between audio modeling and mathematical signal processing, the global objective of the PANAMA project-team is to develop mathematically founded and algorithmically efficient techniques to model, acquire and process high-dimensional signals, with a strong emphasis on acoustic data. Applications fuel the proposed mathematical and statistical frameworks with practical scenarii, and the developed algorithms are extensively tested on targeted applications. PANAMA’s methodology relies on a closed loop between theoretical investigations, algorithmic development and empirical studies.

Created since: : "Pré-équipe" 07/12/12 ; Team 01/01/13
Associated establishments: Inria, Université de Rennes 1, CNRS
Localisation : Campus de Beaulieu, RENNES (35)




The long-term vision of the Rainbow team is to develop the next generation of sensor-based robots able to navigate and/or interact in complex unstructured environments together with human users. Clearly, the word "together" can have very different meanings depending on the particular context: for example, it can refer to mere co-existence (robots and humans share some space while performing independent tasks), human-awareness (the robots need to be aware of the human state and intentions for properly adjusting their actions), or actual cooperation (robots and humans perform some shared task and need to coordinate their actions). Within this general picture, the Rainbow activities will be particularly focused on the case of (shared) cooperation between robots and humans by pursuing the following vision: on the one hand, empower robots with a large degree of autonomy for allowing them to effectively operate in non-trivial environments (e.g., outside completely defined factory settings). On the other hand, include human users in the loop for having them in (partial and bilateral) control of some aspects of the overall robot behavior. We plan to address these challenges from the methodological, algorithmic and application-oriented perspectives.
The main research axes along which the Rainbow team will be articulated are the following: three supporting axes...

  • Optimal and Uncertainty-Aware Sensing;
  • Advanced Sensor-based Control;
  • Haptics for Robotics Applications

...that are meant to develop methods, algorithms and technologies for realizing the central theme of Shared Control of Complex Robotics Systems.

Fact following teams: LAGADIC
Dpt: 5 - Signaux et images numériques, robotique (DSIR)
Created since: 01/01/18
Associated establishments: Université de Rennes 1, CNRS, INSA Rennes,
Location: Campus de Beaulieu, Rennes (35)




Leader: Pierre-François Marteau, PR UBS

Expressiveness in Human Centered Data/Media

EXPRESSION focuses on studying human language data conveyed by different media: gesture, speech and text. Such data exhibit an intrinsic complexity characterized by the intrication of multidimensional and sequential features. Furthermore, these features may not belong to the same representation levels, basically, some features may be symbolic (e.g., words, phonemes, etc.) whereas others are digital (e.g., positions, angles, sound samples) and sequentiality may result from temporality (e.g., signals).

Within this complexity, human language data embed latent structural patterns on which meaning is constructed and from which expressiveness and communication arise. Apprehending this expressiveness, and more generally variability, in multidimensional time series, sequential data and linguistic structures is the main proposed agenda of \team. This main purpose comes to study problems for representing and characterizing heterogeneity, variability and expressivity, especially for pattern identification and categorization.

The proposed research project targets the exploration and (re)characterization of data processing models in three contexts:

Fact following teams:  CORDIAL and SEASIDE
Created since: 13/01/14 Team : 06/02/15
Associated establishments: University of Rennes 1, University of South Brittany (UBS)
Location: Lannion (22) et Vannes (56)


Leader: Anatole LÉCUYER

Hybrid research focuses on multiple user inputs, and intends to exploit both motor activity (motion-tracking) and mental activity (brain-computer interfaces). We want to create novel “body-based” and “mind-based” controls of virtual environments, and reach in both cases immersive and efficient 3D user interfaces. We also want to introduce a “hybrid approach” which will mix mental and motor activities in virtual reality. Hybrid applications are in the field of industry (virtual prototyping), medicine (surgical simulation, rehabilitation), design (architectural mock-ups), digital art, or videogames.

Hybrid follows three main axes of research:

  • Body-based interaction in virtual reality (real-time physical simulation of complex interactive phenomena, haptic and pseudo-haptic feedback)
  • Brain-based interaction in virtual reality (3D user interfaces based on brain-computer interfaces and mind-based control)
  • Hybrid and collaborative 3D interaction (Collaborative virtual environments with multiple users, and shared systems with body and mind inputs).
Created since:  01/07/13
Associated establishments: University of Rennes 1, Inria, INSA Rennes
Location: Campus de Beaulieu, RENNES (35)



Leader: Eric ANQUETIL (Professeur INSA Rennes)

The research topics of IntuiDoc concern the written communication and the engineering of documents under various aspects: analysis, recognition, composition, interpretation and also graphical/gestural man-document interaction. This research relates to the handwriting and the documents under different forms: manuscript, printed paper form, pen-based and touch-based interaction, graph, images, heterogeneous documents, etc.

The roadmap of the IntuiDoc team is on the frontier of several research axes: Pattern recognition, Machine-Learning, Human-Machine Interaction, Uses and Digital Learning. The aim is to explore new scientific challenges of the domain of the Human-Document Interaction with a specific focus to interactive, incremental and evolving learning based on the integration of the user in all the processes of analysis and decision making.

Today, four major emerging scientific axes are investigated with strong partnerships with national and international laboratories and companies:

  • “On-line” evolving cross-learning of 2D (touch and pen –based) and 3D gestures (Kinect and Leap Motion);
  • “On-line” analysis of drawing, sketching and handwriting with pen-based tablet for digital learning (e-education);
  • Interactive learning of document structure without ground-truth;
  • Document collection analysis for big-data.

Created since: 07/11/11
Associated establishments: University of Rennes 1, INSA Rennes
Department: D6 Media and interactions
Location: Campus of beaulieu, RENNES (35)


Leader: Laurent AMSALEG (CNRS)

Creating and exploiting explicit links between multimedia fragments

Linkmedia is concerned with the processing of extremely large collections of multimedia material. The material we refer to are collections of documents that are created by humans and intended for humans. It is material that is typically created by media players such as TV channels, radios, newspapers, archivists (BBC, INA, BnF, …), as well as the multimedia material that goes through social-networks. It also includes material that includes images, videos and pathology reports for e-health applications, or that is in relation with e-learning which typically includes a fair amount of texts, graphics, images and videos associating in new ways teachers and students. It also includes material in relation with humanities that study societies through the multimedia material that has been produced across the centuries, from early books and paintings to the latest digitally native multimedia artifacts.

Multimedia collections are rich in contents and potential, that richness being in part within the documents themselves, in part within the relationships between the documents, in part within what humans can discover and understand from the collections before materializing its potential into new applications, new services, new societal discoveries, …  That richness, however, remains today hardly accessible due to the conjunction of several factors originating from the inherent nature of the collections, the complexity of bridging the semantic gap or the current practices and the (limited) technology. What makes the processing of multimedia collections difficult are e.g. their multimodal nature with a complex blending of texts, images, video and audio and the fact that documents are intertwined since they do not exist in isolation one from the other but rather form a collection. Furthermore, the scale of collections challenges the cost and the quality of any algorithm that runs analysis tasks.

The ambition of Linkmedia is to propose foundations, methods, techniques and tools to help humans make sense of extremely large collections of multimedia material.

Research Areas

Linkmedia follows two main directions of research that are  (i) extracting and representing information from the documents in collections, from the relationships between the documents and from what user build from these documents, and (ii) facilitating the access to documents and to the information that has been elaborated from their processing. Machine learning, AI, computer vision, multimedia analytics, natural language processing, information retrieval and data mining are domains where Linkmedia does research.

Fact following team:  TEXMEX
Created team since: 01/07/14
Associated establishments: Inria, Université de Rennes 1, CNRS, INSA Rennes
Location : Campus of Beaulieu RENNES (35)


Leader: Franck MULTON (Pr. Univ. Rennes 2)

Analysis-Synthesis Approach for Virtual Human Simulation

The main objectives of MimeTIC are to:

  • Address the complexity of real human motions
  • Simulate realistic motions, behaviors and interactions of virtual humans
  • Demonstrate that VR enables better motion understanding and simulation

To reach these objectives, our research is organized into three main axes:

  • Motion analysis: designing innovative protocols and models to enhance knowledge on motion control for complex tasks
  • Autonomous virtual humans: simulating realistic autonomous virtual humans that can interact with their virtual environment as real  humans would do in the same situation
  • Physical activity in virtual reality: using autonomous virtual humans to carry-out experiments on real humans and modifying experimental condition by changing the virtual environment models to improve fundamental knowledge and train people to complex motor skills
Created since: 31/05/13 Team : 01/01/14
Associated establishments: University of Rennes 1, Inria, ENS Rennes
Location: Campus of Beaulieu, RENNES



Leader: Olivier LE MEUR (MCF, Université de Rennes 1)

Computational Visual Perception and Applications

The word percept has a Latin root, i.e. perceptum. It refers to the perception of an object or more generally to the perception of visual phenomena. The human visual system is one of the most important parts of our central nervous system. It gives us the ability to detect and to interpret visual information of our visual environment. The understanding of the mechanisms taking place in the visual system as well as our ability to reproduce them, from a computational standpoint, is a huge challenge.
PERCEPT team aims to understand and to express algorithmically complex phenomena taking place in our visual system. We expect this project to provide decisive results in order to improve the design of new and innovative multimedia applications.

Fact following the teamFRVSENSE
Created since: 13 April 2018
Associated establishments: Université de Rennes 1 
Location : Campus de Beaulieu, Rennes (35)




Leader: David GROSS-AMBLARD & Arnaud MARTIN

DRUID considers models and algorithms for the management and qualification of uncertain, participative, interlinked and large-scale data (social networks, sensor networks, Web streams, crowdsourcing, ...).


Created since: 26/09/2014
Associated establishments: University of Rennes 1
Location : Rennes (35) and Lannion (22)



Leader: Olivier DAMERON (Université de Rennes 1)

DYnamics, Logics and Inference for biological Systems and Sequences

Dyliss is a research team in bioinformatics. We focus on sequence analysis and systems biology. We use qualitative formal systems to characterize genetic actors from non model species, such as algae or mining baceria, that control phenotypic answers when challenged by their environment.

    Methods: constraint logic programming, symbolic dynamics, machine learning, formal systems.
    Expertise: functional characterization, non-model species, multi-scale integration.
    Application domains: marine biology, micro-environmental biology...

Dyliss means truth in the gaelic language.

Created since: 1er juillet 2013
Associated establishments: Inria, Université Rennes 1, CNRS
Location : Rennes – Campus of Beaulieu



Leader: Pierre PETERLONGO

Scalable, Optimized and Parallel Algorithms for Genomics

The GenScale team works in close connection with biologist colleagues to propose algorithms and their implementations to process large genomic data generated by DNA sequencing technologies. Those data are error-prone, scattered, and massive (terabytes of sequences generated within a few days). In this context, GenScale members focus on three main axes:

Analyzing complex features

The team proposes novel approaches to detect genomic variants and to precisely assemble the genome or the chromosome sequences. The ultimate goal is to obtain one sequence per sequenced chromosome or species, together with their associated variations. Techniques are based on algorithms on strings, on graph analyses, on data representation, on linear programming, and ASP solvers.

Exploring and Querying

To scale up the amount of data to be treated, the team proposes new methodological solutions based on advanced data-structures to index and screen large genomic databanks, enabling the detection of specific markers attached to diseases, to the genomics analysis of thousands of full genomes, and to the analyses of gut microbiomes. Techniques are based on data indexation, data correction, and again on algorithms on strings and graphs.

Explore the problem of archiving large volumes of data on DNA molecules, involving problematics such as the development of specific DNA file system, error-correcting codes, information security, DNA synthesis, DNA sequencing, data genomic treatment, etc.


Created since: 13/01/14 Team : 06/02/15
Associated establishments: University of Rennes 1, Inria, CNRS, ENS Rennes
Location: Campus de Beaulieu - Rennes


Leader: Alexandre TERMIER (UR1)

Large Scale Collaborative Data Mining

The objective of the LACODAM team is to considerably facilitate the process of making sense from large quantities of data, either for deriving new knowledge or for taking better actions. Nowadays, this process is mostly manual, and relies on the analyst understanding of the domain, the data at hand and a plethora of complex computational tools. We envision a novel generation of data analysis approaches where the many different ways of discovering structure in data are automatically explored, and only the most relevant structures are shown to the analyst. Such notion of relevance is highly dependent on domain knowledge and analyst’s own knowledge: such knowledge will be central to our approach. The solutions we envision requires to bridge data mining techniques with artificial intelligence approaches, both for taking knowledge into account in a principled way, and to introduce automated reasoning techniques in knowledge discovery workflows. Moreover, in order to acquire as much knowledge as possible, we will investigate community-based approaches, designed for communities of analysts and practitioners working on a given domain, sharing datasets, knowledge and results, and giving feedback.

Fact following team : DREAM
Created since: 01/11/17
Associated establishments : Inria, Université de Rennes 1, INSA Rennes
Location : Campus de Beaulieu, RENNES (35)



Leader: Sébastien FERRÉ (Univesité de Rennes1)

SemLIS = Semantics, Logics, and Information Systems (for Data-User Interaction)

The main objective of the SemLIS team is to bring back to users the power on their data. It aims at facilitating data-user interaction by making users more autonomous and agile, by providing flexibility and expressivity, and yet control and confidence in the information system. It should support users in the semantic representation of heterogeneous data, and in the collaborative acquisition of domain knowledge. The scientific foundations of the team are logics and formal languages for knowledge representation and reasoning, the Semantic Web, information systems, natural language processing, symbolic data mining, and user-data interaction. A key idea is to reconcile the power of formal languages and the usability of natural language and interaction. On the application side, the focus will be put on social sciences and on business intelligence.

Fact following team:  LIS
Created since: 16/09/2016

Associated establishments: Université Rennes 1, INSA Rennes

Location : Campus of Beaulieu, RENNES (35)



Leader: François GOASDOUÉ (Pr. Univ. Rennes 1, Enssat)
Shaman (ex Pilgrim) is the acronym of “A Symbolic and Human-centric view of dAta MANagement”.

Research axis:

  • Modeling/integrating imperfect data
  • Understanding data (Data summaries, database clustering, data analytics)
  • Querying data in a flexible way (Preference queries, Bipolar fuzzy queries, Implementation, fuzzy query optimization, Flexible querying of graph databases)
  • Cooperative answering (Obviating empty/plethoric answer sets, Providing enriched answers)

Shaman is a research team of the Data and Knowledge Management (DKM) department of IRISA.

Fact following team:  PILGRIM
Created since: 01/01/14
Associated establishments: Université de Rennes 1, CNRS
Location : Campus of LANNION  (ENSAAT)



Located in Lannion (Brittany, France) at the facilities of the ENSSAT.