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AYMANE-SNOUSSI/README.md

Hi, I'm Aymane

Machine learning engineer based in Caen, France. I work on computer vision and LLM applications. For my master's thesis at the GREYC lab, I studied how deepfake detectors can be fooled by adversarial attacks.

I'm looking for an ML / AI Engineer position in France.

Projects

Project What it does Built with
adversarial-attacks-rag · live demo Answers questions on research papers about adversarial attacks, in English or French, and cites the paper and page behind every claim. sentence-transformers, Gemini, FastAPI, Streamlit, Docker
network-intrusion-detection Intrusion classifier tested on attack types it never saw in training, explained with SHAP and served as a tested API. scikit-learn, SHAP, FastAPI, Docker
driver-drowsiness-detection Real-time drowsiness monitoring from one camera, deciding on eye closure over time rather than on a single frame. YOLOv8, MediaPipe, OpenCV
ai-personal-trainer Counts repetitions and checks form from a single camera, adapting to each person's range of movement. MediaPipe, YOLOv8, Streamlit

Tools

Python · PyTorch · scikit-learn · OpenCV · Hugging Face · FastAPI · Streamlit · Docker · Git

Contact

LinkedIn · aymansnoussi68@gmail.com

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  1. adversarial-attacks-rag adversarial-attacks-rag Public

    RAG assistant that answers questions on 44 adversarial-attack papers, citing the paper and page behind every claim (FR/EN).

    Python

  2. driver-drowsiness-detection driver-drowsiness-detection Public

    Real-time driver drowsiness detection using PERCLOS and microsleep analysis over time, not per-frame classification. YOLOv8 and MediaPipe compared on the same decision layer.

    Python 1

  3. network-intrusion-detection network-intrusion-detection Public

    Why 99% in a notebook means nothing: network intrusion detection evaluated on unseen attacks, with thresholds and SHAP explanations

    Python

  4. ai-personal-trainer ai-personal-trainer Public

    Repetition counting and form feedback from one camera: an exercise engine with thresholds adapted to each person and 3D angles

    Python