I’m a PhD researcher specialising in deep learning for biomedical signal analysis, with a primary focus on ECG-based atrial fibrillation (AFib) research.
My work targets compact, efficient, and interpretable AI models that support clinically relevant decision-making and screening, bridging rigorous machine learning research with real-world healthcare.
My PhD is pursued at Ghent University (UGent) in close collaboration with imec, within IDLab / SUMO Lab, under the supervision of Prof. Dirk Deschrijver and Prof. Tom Dhaene.
This page provides an overview of my academic background, recent publications, teaching activity, as well as my research projects, collaborations, and funding.
An overview of my key degrees and certifications. This section synchronises periodically with my LinkedIn profile to stay up-to-date.
PhD research in machine learning and deep learning for biomedical time-series, with a strong focus on ECG analysis and atrial fibrillation.
Master’s training focused on AI methods, modelling, and applied machine learning, completed with summa cum laude distinction.
Foundation in software engineering, programming, and practical application development, completed with magna cum laude distinction.
A curated overview of my recent journal articles, conference papers, and academic outputs. The list synchronises periodically with my Google Scholar profile for accuracy. Key themes include compact deep learning architectures, ECG representation learning, and clinically meaningful model evaluation.
A selection of my teaching, supervision, and student support roles, with a focus on computer science education and hands-on learning.
Supervision of 2 Master’s thesis students in biomedical engineering (Inès De Jaegher & Sandrine Doolaege), focusing on both EEG and ECG analysis models.
A series of lectures with 2 practical sessions where students implement baseline security practices such as SSH hardening and IPsec on Linux systems.
Teaching assistant role for 7 practical sessions focused on algorithmic thinking, whiteboard exercises, and structured practice based on standard reference material.
Support and quality control for practical assignments (Jupyter notebooks) spanning topics from Bayesian learning to deep learning, including lab supervision.
Supervision of a Computer Science Master’s thesis student (Daniel Kaloyan), with the objective of translating his high-quality results into an A1-level publication.
Student support role focused on helping new students navigate university life, informed by training on teaching practice and supporting diverse student groups.
Supervision of 3 Master’s thesis students across computer science (Warre Veys & Karel-Brecht Decorte) and biomedical engineering (Vincent Belpaire), with outcomes including strong final grades and follow-up research trajectories.
An overview of my funding, research projects, and academic collaborations. Additional detail is available on my Ghent University profile.
Research project developing deep learning models for signals from a new wearable, supporting prediction and monitoring of cardiovascular conditions in clinical and real-world settings.
Awarded a multi-year research grant to study automated patient trajectory prediction for atrial fibrillation and other arrhythmias using machine learning on standard 12-lead ECG.
Core PhD research track focused on building and evaluating predictive models for biomedical time-series with an emphasis on reliability and clinical relevance.
Responsible for the daily coordination and operational management of HINT.GENT consortium on an ad interim basis. Activities include event organisation, project initiation, stakeholder communication and network development.
Research visit involving collaboration on a novel hybrid-AI approach for disease prediction from ECG signals, together with Prof. Antônio H. Ribeiro.
Collaboration with Ziekenhuis Oost-Limburg (ZOL) and imec focused on developing compact models for atrial fibrillation prediction using hospital-collected ECG data.
Interested in an academic collaboration, project, or talk?