Strong analytical and problem-solving skills for in domain of life (strains and bacteria for food, human and animal healthcare and medical, crops, ...) - Design of experiments - implementation of complex machine learning models for R&D problems solving (optimum detection , enrichment, failure paths, sensitivity, ...) - Collaboration with cross-functional teams all around the world - provide training sessions and e-learning statistics / DoE and ML oriented - design of web-app to enable teams to gain autonomy - Image processing for control

Collaborative industrial projects in the fields of aeronautics, space, defense and automotive. Patent publications (3) and international conferences.Applied data science for industrial use cases.Understand, collect, clean and transform datas to reach required objectives. Create and evaluate algorithms and statistical models to answer the problem.Drive communication with teams in order to make sure that projects are on track. Provide reporting including data visualization to client and management according to deadlines.Continuous improvement: updating standards algorithms, patents …Promote best practices throughout the organization.Outstanding collaborative projects:Project with Thales/Continental/Actia (bonding process): End-to end statistical methodologies (DoE, metamodeling, sensitivity and enrichment) to define optimal and robust operating range of parameters.Project with Safran/ArianeGroup/Thales/+ (surface preparation by laser): Nondestructive control based on image with deep-segmented-NeuralNetwork algorithm for online defects detection and measurement.Project with SafranCeramics/Huguet (impregnation line for aircraft engine application): Predictive maintenance with automated retro control based on LSTM (times series) neural networks.Project with Airbus/Altran/Liebherr/+ (metallic additive manufacturing line): Definition of KPIs from pipelined collected sensors datas and automated defects detection based on machine learning.Skills : • Artificial intelligence• Python – machine learning• Data collecting, processing• Statistics, metamodeling• Image processing, deep learning• Reinforcement learning

Development of models with the aim of improving numerical predictions in the deformation of steel materials under the effect of hot forging, but also subjected to thermal cycles. Development of more predictive models on the rheological parameters of the materials used using inverse optimization methods. Numerical methods (response surfaces via machine learning) applied to data workshops and simulations. Objectives: failure predictions, bypass of business software to save calculation time, observations of influential parameters.

My thesis work carried out at the École Normale Supérieure (ENS) in Cachan focused on thefinite volume formulation of the elasticity equation, in order to develop 2D/3D solvers. Thefield of study is the analysis of small elastic deformations (linear domain) to study theprogression of a frost front in the soil, which introduces heterogeneity and discontinuity. Ideveloped codes mainly in MATLAB and Fortran, which resulted in the development of 4different solvers in 2D and 3D.

Design and development of tools for the M-51 and Ariane V projects.(6-month internship)

Participation in the design of an active hood lifter intended for the automotive market. (6-month internship)