Data&AI Seminar – Khalil Bachiri

Title: Multimodal and Heterogeneous Graph Learning for Robust, Explainable and Frugal Intelligent Systems

Abstract:
The increasing availability of heterogeneous and multimodal data poses significant challenges for the design of modern intelligent systems, particularly in terms of representation, fusion, robustness, explainability, and computational frugality. These challenges become even more critical when data are structured through complex and evolving relational graphs, as is often the case in real-world applications. In this presentation, I will introduce my research contributions on multimodal and heterogeneous graph learning, developed during my PhD, with the objective of designing models capable of understanding, reasoning, and learning from interacting modalities. I will present graph-based and topology-aware learning architectures that explicitly model modality heterogeneity, inter-modal dependencies, and structural relations, while relying on adaptive fusion, alignment mechanisms, and attention-based reasoning to improve robustness, stability, and interpretability. These approaches have been validated on real-world recommendation and decision-support scenarios and have led to several international publications. Finally, I will outline my research perspectives, aiming to further develop robust, energy-efficient, and explainable multimodal intelligent systems, including responsible AI, multimodal platforms, and low-footprint learning for complex environments.

Short Bio:
Khalil Bachiri is a Doctor in Artificial Intelligence from CY Cergy Paris Université (ETIS, CNRS UMR 8051), where he is currently an ATER. His research focuses on multimodal learning and heterogeneous graph learning, topology-aware models, and explainable AI, with an emphasis on robustness, frugality, and multimodal reasoning for recommendation and decision-support systems. He has published in international journals and conferences. He also worked as an AI Research Engineer at CNRS and has been a visiting researcher at LIPN (Université Sorbonne Paris Nord).