Integreat PhD Fellow - UiT The Arctic University of Norway
Academic interests
Youssef’s interests lie at the intersection of medical and healthcare data, with a strong focus on developing advanced representation learning methods and similarity measures. Specifically, in leveraging representation learning techniques and non-Euclidean embeddings to improve the analysis of patient records, biological networks, and clinical decision-making systems. By incorporating domain knowledge—such as the hierarchical relationships between medical diagnoses, the functional connections in molecular interactions, or the contextual significance of clinical pathways—aiming to create more meaningful similarity measures that enhance predictive modeling and knowledge discovery in healthcare. Additionally, exploring how relational taxonomies of medical concepts and patient trajectories can be effectively embedded to refine data augmentation strategies, ultimately improving the robustness and interpretability of machine learning models in medical applications.
Background
Youssef holds a Master of Science degree in Data Engineering and Analytics from the Technical University of Munich (TUM). Passionate about leveraging artificial intelligence in the medical domain, Youssef has contributed to numerous AI research projects in healthcare and medicine, gaining extensive experience across various fields. His expertise includes data preprocessing, feature engineering, signal and image processing, graph analysis, machine learning, and deep learning. With a strong foundation in computational methodologies, he has worked on developing innovative approaches to analysing complex medical data, optimising predictive models, and enhancing healthcare decision-making. Driven by a commitment to bridging the gap between AI research and real-world medical applications, Youssef strives to make a meaningful impact in the field of medical data science.
PhD project Knowledge-driven machine learning
Knowledge-driven representation and similarity learning
Youssef's PhD project focuses on advancing representation learning techniques. His research explores how relationship graphs—extracted from complex datasets such as spatial omics—can capture intricate dependencies beyond pairwise interactions, offering a richer understanding of medical and biological data. Given the challenges posed by varying graph structures and labeled nodes, his work aims to incorporate underlying semantic relationships through knowledge graph embeddings and non-Euclidean geometries. By leveraging these advanced techniques, Youssef seeks to develop more meaningful similarity measures that enhance the analysis of medical and healthcare data, ultimately contributing to improved predictive modeling and decision-making in clinical and biomedical applications.
Supervisors
- Associate Professor Elisabeth Wetzer, UiT The Arctic University of Norway, Department of Physics and Technology
- Associate Professor Benjamin Ricaud, UiT The Arctic University of Norway, Department of Physics and Technology
- Professor Martin Giese, UiO, Department of Informatics
- Reserarcher Vladan Milosevic , UiB, Department of Clinical Medicine