Associate Professor of Computer Science / Accredited to supervise research

Research

Overview

I am interested in machine learning and artificial intelligence, both from a theoretical and a practical standpoint (through research on knowledge transferability, model interpretability, uncertainty, and knowledge representation, and applications in bioinformatics or optimization). Additionally, I am interested in looking into the epistemological and ethical challenges raised by AI.

Keywords (in alphabetic order):

  • AI epistemology and ethics
  • autonomy
  • (deep) machine learning
  • epistemic and aleatoric uncertainty
  • interpretability
  • neuro-symbolic AI
  • transfer learning

List of publications

Scientific supervision

PhD supervision

  • A. Khudiyev (Oct. 2022 - directors: A. Jeannin-Girardon, L. Gardashova) / Neuro-symbolic artificial intelligence
  • Q. Christoffel (Oct. 2021 - Nov. 2024, director: A. Deruyver) / Learning differentiated representations in deep learning models
  • H. Khodji (Oct. 2020 - Nov. 2022, directors: J. Thompson & P Collet) / Characterization of the transferability of latent features in deep neural networks. Application to gene error prediction.
  • N. Scalzitti (Dec. 2018 - Sept. 2021, directors: J. Thompson & P. Collet) / New gene annotation and detection strategies for genome sequencing projects, by massively parallel coupled artificial intelligence and artificial evolution algorithms
  • R. Orhand (Oct. 2018 - Nov. 2022, directors: P. Parrend & P. Collet) / Towards autonomous computers by combination of artificial intelligence and artificial evolution
  • A. Ouskova (Aug. 2018 - May 2022, directors: P. Parrend & P. Collet) / Evolutionary optimization of refrigeration systems using magnetocaloric alloy

Apprenticeship training (graduate students)

  • 2016-2018
    • A. Bruyant / Robust Anonynous DAta Records
    • M. Haegelin / GPU based signal processing optimization for mass spectrometry data

Internships

  • 2023
    • L. Pluot / Le quotidien d’un laboratoire de recherche (grad. student in epistemology and history of sciences)
  • 2022
    • L. Wehrli / Explaining machine learning black-boxes (comp. sci. grad. student) (main advisor: S. Mark-Swecker)
  • 2021
    • Q. Christoffel / Deep Bayesian neural networks (comp. sci. grad. student)
  • 2020
    • H. Khodji / Quantification of the transferability of learned features in deep neural networks (comp. sci. grad. student)
    • Q. Christoffel / Explicability of convolutional neural networks using evolutionary algorithms (comp. sci. grad. student)
  • 2019
    • A. Oury Bah / Hybrid machine learning : combining deep learning models with evolutionary algorithms (comp. sci. grad. student)
    • A. Hutt / quantitative measure of feature transfer in deep neural networks (comp. sci. grad. student)
    • C. Mengel / ATLAS : Algebraic TopoLogy for dAta Similarity (math. grad. student)
  • 2018
    • N. Bannour / Anomaly detection in time series data (main supervisor : N. Lachiche)
  • 2017
    • N. Demeure / B cell regulatory networks modeling (comp. sci. grad. student)
    • F. Delhomme (math. grad. student) and Melchior Villa (comp. sci. grad. student) / Concentric multi-valued map (co-supervisor: Dr B. Sauvage)

Research projects (~150 hrs)

  • 2024-25
    • N. Olejniczak / Non monotonic AI (comp. sci. grad. student)
    • S. Hashemi / Constrained supervised learning of latent representations (comp. sci. grad. student)
    • J. Andreolli / Transfert learning (comp. sci. grad. student) / main supervisor: R. Orhand
  • 2020-21
    • N. Mountasir / Comparing uncertainty quantifications in deep neural networks / co-supervisors : S. Marc-Zwecker and D. Bernhard (comp. sci. grad. student)
    • E. Chetouane / Semantic and artificial neural networks (comp. sci. grad. student)
  • 2019-20
    • Q. Christoffel / Explicability of convolutional neural networks using evolutionary algorithms (comp. sci. grad. student)
    • F. Nawfal / Comparative study of transfer learning methods (comp. sci. grad. student)
  • 2018-19
    • E. Kalbé / Collision detection in virtual environments: comparison of cognitive and geometric approaches (comp. sci. grad. student)
    • G. Mukunde / Deep learning for protein properties identification / co-supervisors: O. Poch & L. Moulinier (comp. sci. grad. student)
    • M. Haller / Deep learning for protein fold classification / co-supervisor: C. Mayer (comp. sci. grad. student)
    • A. Hutt / Multi-task learning, transfer learning and adaptability of deep neural networks / co-supervisor: R. Orhand (comp. sci. grad. student)
  • 2017-18
    • E. Kalbé / Non-representational approaches for agent behavior modeling (comp. sci. grad. student)
    • M. Seyer / Non-representational approaches for agent behavior modeling (comp. sci. grad. student)

Scientific dissemination

  • “Éthique de l’intelligence artificielle en santé: regard sur l’IA en contexte scientifique”, séminaire sur les techniques in silico et intelligence artificielle de l’Institut du Médicament de Strasbourg (IMS), Strasbourg, Novembre 2024
  • “Une perspective épistémologique sur l’intelligence artificielle”, séminaire à l’occasion des 10 ans du laboratoire ICube, Strasbourg, janvier 2024
  • “L’intelligence artificielle comme système socio-techinique”, séminaire des cadres de la fondation Vincent de Paul, Strasbourg, octobre 2023
  • “Intelligence artificielle, éthique et santé”, intervention à la conférence IA de l’ESBS “L’IA au service de la santé ; éthique et cas d’usages”, janvier 2023
  • “Intelligence artificielle : de la technique aux enjeux sociaux”, intervention à la journée doctorale de l’université de Haute Alsace à Mulhouse, juin 2022
  • “Quelle intégration des systèmes intelligents dans nos sociétés ?”, intervention à la table ronde Intelligence artificielle et démocratie, vers quels interactions et enjeux du jardin des sciences de l’Université de Strasbourg, feb. 2020
  • “Transfer learning: review and recent advances” (Nov. 2019), workshop of the ICube research axis Data Science and Artificial Intelligence (DSAI)
  • Talk at the “Journées Système 2018” in Strasbourg (Oct. 2018): “Theoretical and practical challenges of machine learning” (video, in french)
  • Hosting of the complex system stand at the Science Week (“fête de la science”) / With P. Collet (2016 and 2017)