Au sein de NaDI, les chercheurs apportent des soutions innovantes aux nouveaux défis sociétaux posés par la révolution digitale (eGov, eHealth, eServices, Big data, etc.). Issus de différentes disciplines, les chercheurs croisent leurs expertises en informatique, technologie, éthique, droit, management ou sociologie. Regroupant six centres de recherche, le Namur Digital Institute offre une expertise multidisciplinaire unique dans tous les domaines de l'informatique, de ses applications et de son impact social.
Parmi ses principales compétences figurent les méthodes formelles, l'interface homme-machine, l'ingénierie des exigences, les techniques de modélisation pour concevoir des systèmes logiciels complexes, les tests, l'assurance qualité, les lignes de produits logiciels, les bases de données, le big data, l'apprentissage automatique et plus généralement l'intelligence artificielle, la sécurité, la vie privée, l'éthique, l'évaluation technologique et le raisonnement juridique.

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Défense de thèse de doctorat en Sciences biologiques - Mathilde Oger
Neurodevelopmental disruption in zebrafish: investigating the roles of polystyrene micro- and nanoparticles-mediated methylmercury toxicity
Abstract
Plastic pollution has emerged as a pervasive environmental threat, with micro- and nanoplastics (MPs and NPs) accumulating across ecosystems and organisms, including humans. Their ability to adsorb and transport contaminants raises critical concerns for both environmental and public health.
This thesis investigates the developmental neurotoxicity of MPs and NPs in zebrafish (Danio rerio), emphasizing the influence of particle size and mixture toxicity. NPs were shown to cross the embryonic chorion, disrupt physiological functions, and induce anxiety-like behaviour, whereas MPs mainly altered gene expression related to neurodevelopment. When co-exposed with methylmercury (MeHg), NPs enhanced MeHg accumulation in the brain and sensory organs, exacerbating its neurotoxic effects. Notably, the mixture induced severe hypoactivity, impaired lipid metabolism and neurotransmission, and increased mortality.
These findings highlight the critical need to assess plastic particle toxicity not only in isolation but also in environmentally relevant mixtures. NPs, due to their small size and high reactivity, may act as vectors for toxicants like MeHg, amplifying their effects during sensitive developmental stages.
Jury
- Prof. Frédéric SILVESTRE (UNamur), Président
- Prof. Patrick KESTEMONT (UNamur), Secrétaire
- Dr Valérie CORNET (UNamur)
- Prof. Eli THORÉ (UNamur)
- Prof. Jérôme CACHOT (Université de Bordeaux)
- Dr Krishna DAS (Université de Liège)
Inscription obligatoire.
Défense de thèse de doctorat en informatique - Sacha Corbugy
Interaction Through Post-Hoc Explanation in Non-Linear Dimensionality Reduction.
Abstract
In recent decades, the volume of data generated worldwide has grown exponentially, significantly accelerating advancements in machine learning. This explosion of data has led to an increased need for effective data exploration techniques, giving rise to a specialized field known as dimensionality reduction. Dimensionality reduction methods are used to transform high-dimensional data into a low-dimensional space (typically 2D or 3D), so that it can be easily visualized and understood by humans. Algorithms such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE) have become essential tools for visualizing complex datasets. These techniques play a critical role in exploratory data analysis and in interpreting complex models like Convolutional Neural Networks (CNNs). Despite their widespread adoption, dimensionality reduction techniques, particularly non-linear ones, often lack interpretability. This opacity makes it difficult for users to understand the meaning of the visualizations or the rationale behind specific low-dimensional representations. In contrast, the field of supervised machine learning has seen significant progress in explainable AI (XAI), which aims to clarify model decisions, especially in high-stakes scenarios. While many post-hoc explanation tools have been developed to interpret the outputs of supervised models, there is still a notable gap in methods for explaining the results of dimensionality reduction techniques.
This research investigates how post-hoc explanation techniques can be integrated into dimensionality reduction algorithms to improve user understanding of the resulting visualizations. Specifically, it explores how interpretability methods originally developed for supervised learning can be adapted to explain the behavior of non-linear dimensionality reduction algorithms. Additionally, this work examines whether the integration of post-hoc explanations can enhance the overall effectiveness of data exploration. As these tools are intended for end-users, we also design and evaluate an interactive system that incorporates explanatory mechanisms. We argue that combining interpretability with interactivity significantly improves users' understanding of embeddings produced by non-linear dimensionality reduction techniques. In this research, we propose enhancements to an existing post-hoc explanation method that adapts LIME for t-SNE. We introduce a globally-local framework for fast and scalable explanations of t-SNE embeddings. Furthermore, we present a completely new approach that adapts saliency map-based explanations to locally interpret non-linear dimensionality reduction results. Lastly, we introduce our interactive tool, Insight-SNE, which integrates our gradient-based explanation method and enables users to explore low-dimensional embeddings through direct interaction with the explanations.
Jury
- Prof. Wim Vanhoof - University of Namur, Belgium
- Prof. Benoit Frénay - University of Namur, Belgium
- Prof. Bruno Dumas - University of Namur, Belgium
- Prof. John Lee - University of Louvain, Belgium
- Prof. Luis Galarraga - University of Rennes, France
La défense publique sera suivie d'une réception.
Inscription obligatoire.
Défense de thèse de doctorat en informatique - Gonzague Yernaux
An anti-unification based framework for semantic clone detection in Constrained Horn Clauses.
Abstract
Detecting semantic code clones in logic programs is a longstanding challenge, due to the lack of a unified definition of semantic similarity and the diversity of syntactic expressions that can represent similar behaviours. This thesis introduces a formal and flexible framework for semantic clone detection based on Constrained Horn Clauses (CHC). The approach considers two predicates as semantic clones if they can be independently transformed, via semantics-preserving program transformations, into a common third predicate. At the core of the method lies anti-unification, a process that computes the most specific generalisation of two predicates by identifying their shared structural patterns. The framework is parametric in regard with the allowed program transformations, the notion of generality, and the so-called quality estimators that steer the anti-unification process.
Jury
- Prof. Wim Vanhoof - University of Namur, Belgium
- Prof. Katrien Beuls - University of Namur, Belgium
- Prof. Jean-Marie Jacquet - University of Namur, Belgium
- Prof. Temur Kutsia - Johannes Kepler University, Austria
- Prof. Frédéric Mesnard - University of the Reunion, Reunion Island
- Prof. Paul Van Eecke - Free University of Brussels, Belgium
La défense publique sera suivie d'une réception.
Inscription obligatoire.