As algorithmic platforms (in e.g. machine learning, evolutionary computation, constraint programming, planning) increase in complexity to face problem diversity, selecting the best algorithm and/or parameter setting depending on the problem instance at hand becomes a critical issue to deliver decent performances outside the research labs.


Groups
  Learning and Optimization

Joint Inria project teams


Research highlights


Contracts & grants


Software & patents


Collaborations


Members
  SONNERY Hugo

Ph.D. dissertations & Faculty habilitations

° Algorithms for networked systems
° Automated Proof, SMT and Applications
° Automated Reasoning
° Combinatorics
° Compilation and code optimization
° Data-Centric Languages and Systems
° Deductive Verification of Programs
° Digital Fabrication
° Distributed algorithms
° Distributed Design
° Engineering of interactive systems
° Fab lab
° Formal Model-Based Testing
° Formalisation and Proof of Numerical Programs
° Formalisation of (Specification and Programming) Languages in Proof Assistants
° Generative design methods
° Graph Theory
° Green networks
° Heterogeneous Wireless Networks
° High-performance computing
° Human-Computer Interaction
° Integration of Data and Knowledge
° Interaction and visualization paradigms
° Large scale modelling
° Massively distributed algorithms for complex data