About

Work Experience

• Machine Learning Intern, Hydro-Québec Research Institute (IREQ)

  May, 2024 - Jan, 20258 months

  Automated reading of gauges/indicators from various models of electrical equipment.

  • Built and deployed an automated gauge-reading pipeline (integrated into ROS for the inspection robot) to interpret real-world electrical equipment indicators.

  • Contributed to Vision-Language Model (VLM) fine-tuning, handling data preprocessing and annotation with very limited samples to improve gauge understanding.

  • Designed a modular, extensible framework with integrated visualization tools, enabling rapid experimentation and future improvements.

  • Established a structured, annotated data pipeline from robot inspection missions, supporting continuous model training and validation.

• Machine Learning Engineer, MARSS Group

  Dec, 2022 - Jul, 20237 months

  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

  Sep, 2021 - Nov, 20221 year 2 months

  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

  Jun, 2021 - Aug, 20212 months

  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

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