From d9e28ac4dfc00fbe76565904df9c041bbaac35e8 Mon Sep 17 00:00:00 2001 From: Yoan Chabot Date: Wed, 18 Dec 2024 11:54:51 +0100 Subject: [PATCH] Update teaching.md --- _pages/teaching.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/_pages/teaching.md b/_pages/teaching.md index 7d9ce09..2253611 100644 --- a/_pages/teaching.md +++ b/_pages/teaching.md @@ -13,6 +13,11 @@ author_profile: true ## 👩‍🎓👨‍🎓 Students (past) * **[2020-2024]** Thesis co-director of [Lionel Tailhardat](https://genears.github.io/) for his PhD co-supervised with [Raphaël Troncy](https://www.eurecom.fr/~troncy/) ([EURECOM](http://www.eurecom.fr)), "Synergy between knowledge graphs and machine learning for the detection of anomalies" + Here are the four main contributions of his PhD in few words + links: + * A first key contribution is the NORIA-O ontology to model an ICT infrastructure, network topology, logs, events and alarms raised by equipment and operators, procedures to remedy to anomalies. [Paper](https://link.springer.com/chapter/10.1007/978-3-031-60635-9_2), [Code](https://github.com/Orange-OpenSource/noria-ontology) + * A complete pipeline comes with NORIA-O to automatically build a knowledge graph out of dozens of data sources (static and dynamic). ETL is massively used, with declarative mappings developed in RML. Several open source contributions have been made to community software projects (see [here](https://yoanchabot.github.io/code.html) for more info) + * Various synergistic reasoning approaches to detect anomalies. For example, SPARQL queries corresponding to rule patterns leading to anomalies can be tested. Another kind of reasoning performs Process Mining and is implemented using Petri Net. Finally, statistical learning can of course be used. Hence, RDF2Vec embeddings can be computed to feed a classifier that will predict the category of an incident based on the full context. [Paper](https://dl.acm.org/doi/10.1145/3600160.3604991) + * A final contribution is a fully fledged User Interface enabling to browse the knowledge graph as well as execute AI algorithms to detect anomalies and run the various synergistic reasoning methods. [Paper](https://raw.githubusercontent.com/yoanchabot/papers/main/grasec_2024.pdf) * **[2024]** Supervision of Boumediene Sari for his final year internship at [University of Montpellier](https://www.umontpellier.fr/), "Développement de robots pour l'enrichissement de graphe de connaissances d'entreprise" * **[2023]** Co-supervision (main supervisor: [Lionel Tailhardat](https://genears.github.io/)) of [Benjamin Stach](https://benjaminstach.com/) for his final year internship at [UTBM University of Technology](https://www.utbm.fr/), "Development of a solution for collecting and annotating activity traces for an illicit activity detection platform using knowledge engineering and machine learning techniques" * **[2023]** Co-supervision (main supervisor: Antoine Py) of Yassine Trabelsi for his final year internship at [Esprit School of engineering](https://esprit.tn/), "Développement d'outils web de visualisation et d'interrogation de graphes de connaissances"