Nidà Meddouri

Associate Professor

Team

Security and Systems

Campus

Paris

Me in brief

Nidà Meddouri is an Associate Professor at EPITA’s Research Laboratory (LRE, Paris). He focuses on Explainable Artificial Intelligence, Machine Learning, and Cybersecurity Analytics, with a particular interest in interpretable learning models and spatio‑temporal data mining. He has authored over sixty scientific publications and contributes actively to research on explainable cyber‑threat detection, federated anomaly learning, and quantum machine learning.

Short Bio

Nidà Meddouri is an Associate Professor at the EPITA Research Laboratory (LRE, Paris), conducting research in Explainable Artificial Intelligence, Machine Learning, and Cybersecurity Analytics. His work focuses on developing interpretable learning models, particularly through Formal Concept Analysis, ensemble and rule‑based learning, and the analysis of spatio‑temporal and heterogeneous data. He has authored and co-authored more than sixty peer-reviewed publications in international journals and conferences, and he leads several projects on explainable threat detection, federated anomaly learning, terrorism and radicalization modeling, and quantum machine learning. He is actively involved in the scientific community as program chair, organizer of recurring workshops (including the GAST series and SDFIA symposium), reviewer for major AI venues, and editor of scientific proceedings. He also supervises PhD and Master’s research and teaches machine learning, data science, cybersecurity, and algorithms across multiple academic programs..

Research Interests

Machine Learning & Explainable AI

  • Interpretable learning models
  • Rule-based classification
  • Ensemble learning and boosting
  • Supervised and semi-supervised concept learning
  • Formal Concept Analysis (FCA) for interpretable AI

Cybersecurity & Threat Intelligence

  • Explainable and interpretable threat detection
  • Network intrusion and anomaly detection
  • Stream mining for real-time attacks
  • Learning from terrorist attack and radicalisation datasets
  • Federated learning for cybersecurity

Data Mining & Knowledge Discovery

  • Temporal and spatio-temporal data mining
  • Heterogeneous temporal data analysis
  • Molecular data analysis and cheminformatics
  • Adaptive clustering and adaptive K-means
  • Big data processing (WEKA, MOA, DistributedWekaSpark)

Quantum Machine Learning

  • Quantum FCA
  • Leakage-reduction strategies for superconducting quantum gates
  • Optimization of quantum circuits

Blockchain & Digital Forensics

  • ML-based blockchain transaction identification
  • Shadow ML detection in operating systems

AI for Society & Behavioural Modeling

  • Crime analysis in France
  • Terrorist behaviour modeling (GTD)
  • Radicalisation pattern discovery (PIRUS)
  • Decision-rule learning for societal data

Teaching

ARTIFICIAL INTELLIGENCE & DATA SCIENCE

  • Artificial Intelligence
  • Introduction to Machine Learning
  • Machine Learning and Decision Making
  • Stream Mining
  • Big Data Clustering
  • Business Intelligence Specialist
  • Knowledge Representation and Extraction
  • Decision Support and Artificial Intelligence
  • AI-Driven Threat Detection and Response
  • Intermediate Python

PROGRAMMING & SOFTWARE DESIGN

  • C Programming Workshop
  • C++ Programming Workshop
  • Object-Oriented Programming
  • Basics of Object-Oriented Programming
  • Programming Languages
  • Compilation and Language Implementation
  • Advanced Software Design
  • Design Methods
  • Web Applications: Design, Development & Management
  • Basics of Web Design (HTML5 & CSS3)
  • Advanced Algorithms
  • Algorithms and Data Structures
  • Algorithm Complexity
  • Parallel Programming

NETWORKS & SECURITY

  • Network Elements I
  • Network Elements II
  • Operating Systems
  • Network Operating Systems
  • System Security
  • Computer Security
  • Web Security
  • Information Systems Security
  • Information Systems
  • The Global Network

INFORMATION SYSTEMS & MANAGEMENT

  • Information Systems Strategy
  • Introduction to Management Information Systems
  • Management Information Systems (MBA)
  • Introduction to Enterprise Resource Planning Systems
  • Electronic Commerce
  • Information Systems (Core Curriculum)

CROSS-CUTTING MODULES

  • ICT Certification (C2i)
  • Scientific Presentation
  • Supervised Personal Work
  • Digital Ethics
  • Project Coaching (Game/Tool Development)

Publications

International peer-reviewed journal articles

  • [J3] Lejmi M., Geslin D., Bureau R., Cuissart B., Ben Slima I., Meddouri N., Borgi A., Lamotte J.-L., Lepailleur A. Navigating pharmacophore space to identify activity discontinuities: A case study with BCR-ABL. Molecular Informatics, 2024.
  • [J2] Meddouri N., Khoufi H., Maddouri M. DFC: a performant dagging approach of classification based on concept lattice. IJAIML, 2021.
  • [J1] Meddouri N., Maddouri M. Efficient closure operators for FCA based classification. IJAIML, 2020.

International peer-reviewed conference papers

  • [C9] Souissi Y., Boissier F., Meddouri N. CNC-TP: Classifier Nominal Concept Based on Top-Pertinent Attributes. ICTAI 2025, Athens.
  • [C8] Meddouri N., Beserra D., Salmon L., Adja E. Decision Rule-Based Learning of Terrorist Threats. IC3K-KDIR 2025, Marbella.
  • [C7] Lejmi M., Geslin D., Bureau R., Cuissart B., Ben Slima I., Meddouri N., Borgi A., Lamotte J.-L., Lepailleur A. Generating local rules in Fuzzy Rule-Based Classification Systems. ICCSA 2025.
  • [C6] Azibi H., Meddouri N., Maddouri M. Survey on FCA-based supervised classification techniques. ICMLIS 2020, Seoul.
  • [C5] Trabelsi M., Meddouri N., Maddouri M. A new feature selection method for nominal classifier based on FCA. KES 2017, Marseille.
  • [C4] Meddouri N., Khoufi H., Maddouri M. Parallel learning and classification for rules based on formal concepts. KES 2014, Gdynia.
  • [C3] Meddouri N., Khoufi H., Maddouri M. Diversity analysis on Boosting Nominal Concepts. PAKDD 2012, Kuala Lumpur.
  • [C2] Meddouri N., Maddouri M. Adaptive learning of nominal concepts for supervised classification. KES 2010, Cardiff.
  • [C1] Meddouri N., Maddouri M. Boosting Formal Concepts to Discover Classification Rules. IEA/AIE 2009, Tainan.

International peer-reviewed workshop papers

  • [W2] Fray F., Meddouri N., Maddouri M. Cloud implementation of CNC using DistributedWekaSpark. BigFCA 2019.
  • [W1] Trabelsi M., Meddouri N., Maddouri M. New taxonomy of classification methods based on FCA. FCA4AI 2016.

Francophone peer-reviewed conferences

  • [C6fr] Lejmi M., Geslin D., Bureau R., Cuissart B., Ben Slima I., Meddouri N., Borgi A., Lamotte J.-L., Lepailleur A. Raffinement de l’activité des molécules. SFCi 2023.
  • [C5fr] Beserra D., Meddouri N., Restes C., Nait Zerrad A., Bouharicha B., Duvernoy A. Performance vs cost ratio of Raspberry Pi. COMPAS 2023.
  • [C4fr] Trabelsi M., Meddouri N., Maddouri M. Nouvelle taxonomie des méthodes de classification ACF. CARI 2016.
  • [C3fr] Meddouri N., Khoufi H., Maddouri M. Apprentissage parallèle pour règles ACF. RFIA 2014.
  • [C2fr] Meddouri N., Khoufi H., Maddouri M. Critère d'arrêt de Boosting basé sur diversité. INFORSID 2011.
  • [C1fr] Meddouri N., Maddouri M. Règles de classification par dopage de concepts. EGC 2009.

Francophone symposium papers

  • [S2fr] Mouhamad M., Alaeldine A., Ouffoue G., Meddouri N., Verna D., Parrend P. Arbres de décision pour anomalies réseaux. SDFIA 2025.
  • [S1fr] Lejmi M., Geslin D., Bureau R., Cuissart B., Ben Slima I., Meddouri N., Borgi A., Lamotte J.-L., Lepailleur A. Clustering en chémoinformatique. CS-UTM PhD-Symp 2023.

Francophone peer-reviewed workshops

  • [W7fr] Meddouri N., El Ajouz K., Salmon L., Beaudoin L. Criminalité en France (2012–2021). GAST-EGC 2026.
  • [W6fr] Meddouri N., Salmon L., Gesbert S., Lee-Song-Yin L., Grogan N., Harlit K. Radicalisation USA (1948–2022). GAST-EGC 2026.
  • [W5fr] Meddouri N., Salmon L., Girard T., Elie-dit-Cosaque C., Gradinac L., François C., Chennoufi A. Attentats mondiaux (1970–2020). GAST-EGC 2026.
  • [W4fr] Meddouri N., Besserra D., Salmon L., Paviot T. Menaces d'attentats en France (2012–2021). GAST-EGC 2025.
  • [W3fr] Meddouri N., Besserra D. Criminalité en France (2012–2021). GAST-EGC 2024.
  • [W2fr] Meddouri N., Rioult F., Cremilleux B. Données temporelles hétérogènes. GAST-EGC 2022.
  • [W1fr] Khalfi B., Cherif R., Meddouri N., Maddouri M. Méthodes FCA sous WEKA. EGC 2010.

Posters

  • [P3] Azibi H., Meddouri N., Maddouri M. Méthodes de classification fondées sur l’ACF. IC 2020.
  • [P2] Meddouri N., Maddouri M. Concepts formels pour apprentissage supervisé. EGC 2010.
  • [P1] Meddouri N., Maddouri M. Classification methods based on FCA. CLA 2008.

Proceedings (editor)

  • [A5] Meddouri N., Iphar C., Leborgne A., Salmon L., Agoun J. 11th GAST Workshop (2026).
  • [A4] Meddouri N., Iphar C., Leborgne A., Salmon L. SDFIA 2025.
  • [A3] Meddouri N., Iphar C., Leborgne A., Salmon L. 10th GAST Workshop (2025).
  • [A2] Meddouri N., Leborgne A., Salmon L. 9th GAST Workshop (2024).
  • [A1] Leborgne A., Salmon L., Meddouri N. 8th GAST Workshop (2023).

Thesis & dissertation

  • [T] Meddouri N. Apprentissage d’Ensemble de Règles par Analyse de Concepts Formels. PhD, 2015.
  • [M] Meddouri N. Étude de l'apprentissage des règles par Boosting de concepts formels. Master, 2009.