I obtained my PhD in Advanced Hemodynamic Modelling at the Department of Anesthesiology of Amsterdam UMC and currently work as a postdoctoral researcher. I am a Technical Physician with a strong focus on translating complex physiological data into clinically actionable insights.

My work centers on the development of machine learning–based algorithms to estimate the probability of individuals having a disease (diagnostic applications) or developing a disease or health state over time (prognostic applications). By combining physiological modeling with clinical data, the goal is to support more accurate and timely clinical decision-making.

My research is embedded within the Anesthesiology and Intensive Care departments, where continuous monitoring, hemodynamics, and critical care medicine converge. The focus lies on developing robust, interpretable, and clinically applicable models that can be deployed in real-world, high-acuity clinical settings.

A key objective of this work is to bridge the gap between technical innovation and clinical practice by translating advances in machine learning and computational modeling into practical tools that integrate seamlessly into clinical workflows and ultimately improve patient outcomes.

Hemodynamics, Cerebral autoregulation, Machine learning, Modeling