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Maxime Labonne · @maxime-labonne

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Linkedin Publications

From linkedin.com/in/maxime-labonne

I maintain a concise record of my scholarly output, professional service, recognitions, language skills, and intellectual property. This includes fourteen peer‑reviewed publications spanning model merging, topological data analysis, spam detection, network emulation, formal dataset verification, bandwidth forecasting, SDN flow admission, intrusion detection, and vehicular communication security.

  • Publications: 14 items (2024‑2018) covering model merging, topological separability, Spam‑T5, dynamic link emulation, formal verification, bandwidth forecasting, PFAR, and intrusion detection.
  • Bandwidth forecasting (Random Forest) improves MAE by +19.73 % and RMSE by +18.00 % over the baseline.
  • PFAR heuristic reaches ≈95 % of the ILP optimal solution within a fixed time budget.
  • PFAR exact ILP finds optimal solutions in seconds (even milliseconds) for networks up to 50 nodes.
  • Intrusion detection on KDD Cup 99 achieves 94.44 % accuracy with 0.33 % false‑positive rate.
  • Protocol‑header IDS detects 7 out of 11 unseen attacks on CICIDS2017 with 0 % false positives.
  • Volunteer service: Google Developer Expert (AI/ML) since Apr 2024.
  • Awards: ICSSS Challenge winner (Aug 2019), Europol Challenge “Best project” (Aug 2019), Medal of Merit from French DGA (Aug 2016), Nuit de l’info web‑development Gold (2014) and Silver (2015).
  • Languages: English (full professional), French (native), German (limited working).
  • Patents: Six filings, including US‑2025‑0272218‑A1 (code‑quality automation, Aug 28 2025) and US‑2024‑18/543,487 (quantum‑key distribution transmission, Jun 20 2024).