I got a Master degree in 2002 in Computer Science(fr) from the INSA de Rennes (fr) (National Institute of Applied Science). The same year, I got a DEA (Master degree more dedicated to researcher).
I worked with Pierre-Yves Glorennec during summer 2001 on fuzzy logic and regression tree.
I did my Master thesis under the supervision of Marie-Odile Cordier and Christine Largouet on the topic of using model-checking techniques to solve diagnosis problems. In October 2002, I started my PhD under the supervision of M-O Cordier and Ch Largouet on decentralised and incremental diagnosis of reconfigurable discrete-event systems. I worked with two roommates: Elisa Fromont (mostly fr) and Francois Portet. I defended my thesis one week after them (grumpf!) in December 2005. During my PhD and my Master, I did some teaching (introduction to algorithmic, advanced course on compilation, artificial intelligence).
My main research topic is the diagnosis of discrete-event systems. The basic idea is the following. Consider a system (for instance a machine, such as a computer, a car, a space robot, or machine in a factory, etc.) which performs some actions. The system is subject to faults (such as short-circuit, leaking, break of a component, etc.) which leads to an uncorrect behaviour of the system. The goal is to use the observations (alarms generated by the system, informations provided by sensors, etc.) on the system find out what happened on the system. More precisely, I am interested in discrete-event systems. Such systems are so that their behaviour is not continuous but can be modeled as a discrete evolution (by events). A set of behaviours on such a system can be represented by an automaton. For instance, the model is often an automaton (or an equivalent representation). I often define the diagnosis as the computation of all the possible behaviours on the system consistent with the observations. This can also be represented by an automaton. The problem is quite well defined, and the main issue is the complexity which is exponential in the number components in the system.