Olivier Dameron

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Maître de conférences université de Rennes 1

Office D150
Dyliss team, Irisa / Inria Rennes-Bretagne Atlantique,
Campus de Beaulieu, 35042 Rennes Cedex, France.
Tel: +33 (0) 2 99 84 [74 46]
Fax: +33 (0) 2 99 84 71 71
Email: [olivier.dameron@univ-rennes1.fr]
GPG public key fingerprint is : ADC4 560F 2D32 B816 4500 3805 9651 5116 EF1A 0911
ORCID: http://orcid.org/0000-0001-8959-7189


I am an associate professor at Rennes1 university. As of 2013, I joined the Dyliss team.



If you need to reach me, you can have a look at my agenda.



I develop ontology-based methods for analyzing biomedical data. It relies on knowledge representation and bioinformatics.

My approach consists in leveraging symbolic domain knowledge for improving the analysis of massive, complex, highly interdependent and usually incomplete data. I use Semantic Web technologies for integrating these data and for combining different kinds of reasoning such as deduction, classification or similarity-based comparison.

My main domain of application is functional characterization and comparison of metabolic and signaling pathways. For achieving this, I am principal investigator on the following projects:

  • AskOmics a visual SPARQL query interface supporting both intuitive data integration and querying while shielding the user from most of the technical difficulties underlying RDF and SPARQL. The underlying motivation is that even though Linked (Open) Data now provide the infrastructure for accessing large corpora of data and knowledge, life sciences end-user seldom use them, nor contribute back their data to the LOD cloud by lack of technical expertise. AskOmics aims to bridge the gap between end users and the LOD cloud.
  • FederatedQueryScaler: for determining the optimal decomposition of SPARQL federated queries. It is an INRIA "Exploratory Research Project" (2017-2019) in collaboration with the WIMMICS team in Sophia-Antipolis. Data analysis often requires to combine the user's data with data from other sources, and Linked Open Data is a key element for achieving this. However, it typically results in rich SPARQL queries over several endpoints, and these queries' performances are currently a major bottleneck. The solution developed in the FederatedQueryScaler project is domain-independent, but the projects Dyliss is invlded in will provide relevant use cases.

Here is my list of publications.



  • I supervize Univ. Rennes 1's master degree in bioinformatics with Emmanuelle Becker (from IRSET). In particular, I am responsible for:
    • M1 introductory course on computer systems
    • M1 imperative programming in Python
    • M2 big data and Semantic Web
    • M2 internships
  • e-health and medical data in the 3rd year of ESIR engineering school
  • L2 introduction to bioinformatics at ISTIC (computer science department)
  • DFGSM2 (2nd year) class on bioinformatics for genomics at the school of medicine (with Marie de Tayrac)