Two Papers accepted at POPL 2020

Two research papers from members of the LoVe team were accepted for publication at the…

FSCD-IJCAR 2020 joint Conference

The conferences "Formal Structures for Computation and Deduction" (FSCD) and "International Joint Conference on Automated…

LIPN-Paris13 project of a Research University School in Mathematics and Computer Sciences was selected for funding!

An international jury, chaired by Sir Malcolm Grant, until recently President of NHS England and…

A3

Our group covers a remarkably broad range of topics, from Statistical Learning to Graph Mining and Reinforcement Learning. This allows us to be quite reactive to cope with new challenges raised by emerging applications of Machine Learning. It also makes it possible to study innovative combinations of learning methods for tackling complex problems.

AOC

The AOC team was issued in 2010 from two research domains of the former OCAD team: Combinatorial Optimization, and Parallel and Distributed Computing. The team is organized along three axes: optimization on graphs, mathematical programming and parallel and distributed computing. An interesting characteristic of the AOC team is that its expertise covers a broad spectrum of topics either horizontal (from graph theory to heuristics) or vertical (from algorithm design to detailed implementation). It is unusual in Europe and all over the world to find an equivalent spectrum.

CALIN

En 2010, le LIPN a décidé de créer une équipe de combinatoire, dans un effort pour rassembler des chercheurs venant de différents domaines de la combinatoire, que ce soit en informatique, mathématiques ou physique. Cette combinaison de différents savoirs et expériences offre un large panel de techniques pour attaquer des problèmes difficiles venus de chacune de ces communautés. Cette philosophie se retrouve dans l’acronyme de l’équipe, CALIN, pour Combinatoire, ALgorithmes et leurs INteractions.

LoVe

Our team is structured around two autonomous axes of research :

• Types, models and theory of programming
• Specification, and modular and distributed verification

RCLN

The RCLN team is interested in the expressive power of natural language and its impact on knowledge representation. This work includes fundamental and applied research with an original combination of skills in natural language processing and knowledge engineering. The team's project aims to articulate knowledge discovery and retrieval to be applied on semantic web technologies.