Team Member

Mathilde is a PhD candidate combining her experience in econometrics and data science with her passion for healthcare. Her research focuses on risk prediction within the heart failure patient-population using real-world data and advanced machine learning techniques.

She holds an MSc in Health Data Science from the Harvard T.H. Chan School of Public Health, where she trained in statistical and machine learning methods for healthcare, and prior to that, she completed both her BSc and MSc in Econometrics at Erasmus University Rotterdam, graduating with honors and a specialization in Business Analytics.

Her research experience includes developing and evaluating sparse estimation methods for parsimonious clinical prediction models, as well as constructing clinical knowledge graphs to enable patient similarity analyses and graph-based learning in medical research.

Currently, she focuses on the development and implementation of clinical AI-models to support evidence-based decision-making. Mathilde’s work is driven by a keen interest in personalized medicine, trustworthy AI, and the integration of data science tools into clinical workflows.