Projects using Python in Les UlisProjects using Python in Les Ulis
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Cover image for 2 Peer-Reviewed Publications — ML
2 Peer-Reviewed Publications — ML for Biomedical Classification Co-authored two peer-reviewed papers on applied machine learning and deep learning in clinical and biological contexts, working with CNRS, CEA, and Université Côte d'Azur. Paper 1 — Bioinformatics: Introduced PD-CR, a novel primal-dual classification method outperforming SVM, Random Forests, and PLS-DA on cancer metabolomics datasets (lung & brain tumors). Real clinical data from university hospitals of Nice and Montpellier. Paper 2 — IEEE/ACM Transactions on Computational Biology and Bioinformatics: Non-invasive live cell cycle monitoring using a supervised autoencoder on Quantitative Phase Imaging data. Achieved 98.6% classification accuracy on 80,000+ cells. Both projects used real clinical datasets. No simulations.
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Cover image for Collaborative project built at Kaggle
Collaborative project built at Kaggle Week 26 GenAI Hackathon, contributed the core AI engineering layers of a Business Law RAG Assistant that answers legal document queries with source citations and live faithfulness verification. My contributions: built the hybrid FAISS + BM25 retrieval pipeline for accurate document search; designed the multi-agent orchestration system coordinating retrieval, generation, and verification agents; implemented the faithfulness verification layer to evaluate and score LLM output quality; integrated an MCP tool server enabling live web search as a fallback; and built the analytics dashboard tracking query performance and retrieval metrics. Built as part of a 4-person team, my work covered the RAG pipeline, agentic architecture, and evaluation infrastructure.
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