Young doctor in computer science, I obtained my Ph.D. at LAMSADE (CNRS UMR 7243) in Paris-Dauphine University. I am interested in machine learning, graph theory, lattices and complexity. My thesis deals with learning, modeling, and preferences prediction.

I focus on conditional preferences networks, also called CP-nets, and their learning when data are corrupted by noise. I also work on formal concept analysis (FCA) in order to find a link between lattices and CP-nets (preference mining).

Finally, I study an efficient learning task to learn some parameters of a multicriteria decision method called MR-Sort by using support vector machines (SVM). This method rank alternatives in totally ordered classes.

Keywords : Lattice theory, graph theory, complexity, (Graph) preference learning and mining, conditional preference networks, noisy data.

My academic CV (in english) is available here.

A PDF version of my PhD manuscript (in french) is also available here.

Video of my presentation (in french) during my Ph.D. defense (27/09/2018) in Université Paris-Dauphine (slides, in french, are available here):
the video
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Video of the defense’s questions (in french):

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