Celine Vens
Scholar

Celine Vens

Google Scholar ID: 20Nsc-4AAAAJ
professor, Katholieke Universiteit Leuven
machine learningexplainable AImulti-output predictionhealthcare
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Citations & Impact
All-time
Citations
2,551
 
H-index
25
 
i10-index
45
 
Publications
20
 
Co-authors
0
 
Contact
CVOpen ↗
Publications
3 items
Oxytrees: Model Trees for Bipartite Learning
2025
Cited
0
Pairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation
2025
Cited
0
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
2025
Cited
0
Resume
Academic Achievements
  • [06/2025] PhD student Michela Venturini defended her thesis on adverse event prediction in ICU using ML
  • [09/2024] PhD students Jasper de Boer and Klest Dedja successfully defended their theses
  • [09/2024] First place in the DREAM Olfactory Mixtures Prediction Challenge
  • [06/2024] Second place in the 6th Digital Critical Care Datahon (ESICM)
  • [12/2023] FWO project on structured output learning for healthcare applications approved
  • [05/2023] Felipe Kenji Nakano awarded FWO postdoctoral mandate
  • [01/2023] PhD student Fateme Nateghi Haredasht defended thesis on predictive models for AKI patients; accepted for postdoc at Stanford University
Research Experience
  • PhD supervisor of Robbe D'hondt, Achilleas Ghinis, Pedrio Ilídio, Tingting Shao, Louis Van Slambrouck, and Hibba Yousef
  • Postdoc researchers in her group: Alireza Gharahighehi, Felipe Kenji Nakano, and Lucy Van Kleunen
  • Former predoc/postdoc members include Konstantinos Pliakos, Fateme Nateghi, Jasper de Boer, Klest Dedja, Michela Venturini, Kazeem Dauda, and Mainul Quraishi
  • Supervises Master’s theses in Statistics and Data Science, AI, and Medicine
  • Programme director of the Bachelor in Biomedical Sciences at KULAK (2018–2022)
Background
  • Professor at the Faculty of Medicine, KU Leuven KULAK, Belgium, with a background in computer science
  • Affiliated with two research groups: the Data Driven Healthcare group (Biomedical Sciences, KULAK) and the interdisciplinary itec group (KU Leuven & imec)
  • Main research interest: developing machine learning algorithms for healthcare applications, especially automatic knowledge extraction from large biomedical datasets
  • Focuses on predictive models for clinical decision support to advance personalized and preventive healthcare
  • Research keywords: multi-output learning (multi-label/multi-target/hierarchical prediction), tree-based ensemble learning, explainable models, interaction prediction and recommender systems, supervised/unsupervised/semi-supervised learning, survival analysis
  • Member of Leuven.AI (KU Leuven Institute for Artificial Intelligence) and co-PI of the Flanders AI Research Program