$$ \newcommand{\dint}{\mathrm{d}} \newcommand{\vphi}{\boldsymbol{\phi}} \newcommand{\vpi}{\boldsymbol{\pi}} \newcommand{\vpsi}{\boldsymbol{\psi}} \newcommand{\vomg}{\boldsymbol{\omega}} \newcommand{\vsigma}{\boldsymbol{\sigma}} \newcommand{\vzeta}{\boldsymbol{\zeta}} \renewcommand{\vx}{\mathbf{x}} \renewcommand{\vy}{\mathbf{y}} \renewcommand{\vz}{\mathbf{z}} \renewcommand{\vh}{\mathbf{h}} \renewcommand{\b}{\mathbf} \renewcommand{\vec}{\mathrm{vec}} \newcommand{\vecemph}{\mathrm{vec}} \newcommand{\mvn}{\mathcal{MN}} \newcommand{\G}{\mathcal{G}} \newcommand{\M}{\mathcal{M}} \newcommand{\N}{\mathcal{N}} \newcommand{\S}{\mathcal{S}} \newcommand{\diag}[1]{\mathrm{diag}(#1)} \newcommand{\diagemph}[1]{\mathrm{diag}(#1)} \newcommand{\tr}[1]{\text{tr}(#1)} \renewcommand{\C}{\mathbb{C}} \renewcommand{\R}{\mathbb{R}} \renewcommand{\E}{\mathbb{E}} \newcommand{\D}{\mathcal{D}} \newcommand{\inner}[1]{\langle #1 \rangle} \newcommand{\innerbig}[1]{\left \langle #1 \right \rangle} \newcommand{\abs}[1]{\lvert #1 \rvert} \newcommand{\norm}[1]{\lVert #1 \rVert} \newcommand{\two}{\mathrm{II}} \newcommand{\GL}{\mathrm{GL}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\grad}[1]{\mathrm{grad} \, #1} \newcommand{\gradat}[2]{\mathrm{grad} \, #1 \, \vert_{#2}} \newcommand{\Hess}[1]{\mathrm{Hess} \, #1} \newcommand{\T}{\text{T}} \newcommand{\dim}[1]{\mathrm{dim} \, #1} \newcommand{\partder}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\rank}[1]{\mathrm{rank} \, #1} $$

As a professor in Applied Mathematics at Ecole Nationale Supérieure d'Arts & Métiers (Lille campus), I'm exploring Artificial Intelligence algorithms for industrial applications such as robotics. Since my phD (1997), I'm very interested by mathematical tools dedicated to real world. Artificial Intelligence (AI) is likely one of the corner stone to tackle competitiness and human challlenges faced by our society. Indeed, we are entering a new age of Artificial Intelligence applications. Machine Learning is the core technology, but it is opaque, non-intuitive, and difficult to understand. The effectiveness of AI systems is limited by the machine’s current robustness and inability to explain their decisions and actions to human users. Moreover, companies are facing challenges that demand more intelligent, autonomous, and symbiotic AI systems.
Email
olivier.gibaru@ensam.eu

Google Scholar
Research Experience
Coordinator (2016-2019) of the european H2020 robotic project ColRobot involving 5 countries and 11 research/industrial partners (4,3 M€ budget)

Since 2016, member of the scientific IEEE-IES Subcommittee on Computer Vision and Human-Machine Interaction in Industrial and Factory Automation

Expert in 2016 for the French Academy of Sciences and French Academy of Technologies group concerning the French robotic future

Member of the "Comité de liaison" SIGMA of the French Society for Industrial and Applied Mathematics

I was member (2007-2017) of the INRIA-ALIEN research group conducted by Michel Fliess (Ecole Polytechnique)
International Research Collaborations
Mickael Wolf (NASA JPL)
Byron Boots (GeorgiaTech Atlanta - NVIDIA)
Pedro Neto (Coïmbra University)
John Lavery (US Army)
Shu-Cherng Fang (North Carolina State University)
Taous-Meriem Laleg (KAUST University)
Emmanuel Brousseau (Cardiff University).

Research interest

Geometry
Robotics
Artificial Intelligence
Automatic
Lean Manufacturing

Industry Projects - 4M€

SERIMAX(2017-2019), RENAULT (2016-2019), THALES (2016-2019), SAFRAN (2018)
Robotics projects

Awards

Safran Innovation Awards: Open Innovation Prize (2019)
Arts & Métiers Foundation Prize (2017)

Education

University of Lille, France

Habilitation thesis to supervise research in the field of Applied Mathematics, 2004
Title: Rational surfaces with singularities. Application to Computer Aided Geometric Design

Ecole Nationale Supérieure d'Arts & Métiers, France

phD in Applied Mathematics, 1997
Diploma in mechanical engineering, 1993

University of Pierre et Marie Curie, France

M.Sc., Robotics, 1993

Press articles