The pervasive theme of data science and big data is faced by A&O/TAO in the context of autonomic computing and grid computing for e-Science. In the first case, the point is to model and ultimately manage a 100,000+ core system (the EGEE grid). In the second case, the point is to exploit data from scientific experiments to support the scientist search for hypotheses compatible with the available data and new experiments to discriminate among these hypotheses.
Groups
Learning and Optimization
Joint Inria project teams
Research highlights
Contracts & grants
Software & patents
Collaborations
Members
YE Lina GUYON Isabelle FERRE Arnaud
Ph.D. dissertations & Faculty habilitations
