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Machine Learning Engineer

MARSS Group

Develop/Implement production-ready Object Detector model (mainly one-stage CNNs) & Compress those lasts for edge low-power devices deployment

• Deep Learning to the edge
• Computer Vision - Object Detection
• Neural Network Compression

Data Scientist Apprenticeship

AzurIA

Still at its very beginning, I joined AzurIA to support the R&D-team's AI projects. I had the opportunity to work on many Computer Vision-related projects.

• Life of the creation of a startup
• Computer Vision - Satellite Imagery/Remote Sensing, Cloud Segmentation (Custom U-Net), Boat Detection (YOLOvx model series)
• Literature Survey, State-of-the-art of DL datasets/algorithms applied to multi-spectral images
• Anomaly Detection - Defect detection manufactured pieces, Disease Segmentation on vign leafs
• DevOps/MLOps - Self-hosted ML workspace & GPU cluster maintenance, Gitlab CI/CD, PoC of ML projects
• TinyML - Deployment ML models on edge devices (several nvidia jetsons, raspberry pi/pico, intel Movidius/OpenVino toolkit)

Data Scientist Intern

Center Antoine Lacassagne, CHU Nice - Department of Epidemiology, Biostatistics and Health Data

This internship was to develop and validate a machine learning model to help in the decision of malignancy of a thyroid nodule. Then, the performance of several machine learning methods were compared.

• Machine learning for medical diagnosis in oncology
• Publication of a scientific article in a peer-reviewed journal

Tutor

University of Montreal - DIRO Department

Animator of weekly seminar on 'Learning Linux' for students. I also help (undergrad) students with their homeworks and projects.

• Linux Seminar for students
• Projects/Homework assistance, tutoring

Professional Diploma

MILA Institute / University of Montreal

D.E.S.S. in Machine Learning

• Representation Learning
• Machine Learning Fundings
• Data Science
• Advanced Deep Learning projects

Baccalaureate + Master's degree

Polytech Nice Sophia-Antipolis

Applied Mathematics and Modeling - Data Science

Hackaton - 2nd place

BonsAPPs/BonsEyes european organization

AzurIA R&D team has successfully participated in Stage 1 of the 1st support programme of BonsAPPs. Throughout a two-month program, we developed a structured datatool and an AI Asset workflow for the training and deployment of deep neural network on the edge for a Defect Detection usecase.

Application of machine learning methods to guide patient management by predicting the risk of malignancy of Bethesda III-V thyroid nodules

European Journal of Endocrinology

Indeterminate thyroid nodules (ITN) are common and often lead to (sometimes unnecessary) diagnostic surgery. We aimed to evaluate the performance of two machine learning methods (ML), based on routinely available features to predict the risk of malignancy (RM) of ITN