Projects
Towards a mechanistic understanding of decision confidence KU Leuven
Towards a mechanistic understanding of confidence KU Leuven
When making a decision, we often experience a sense of confidence in the accuracy of that decision. Such metacognitive evaluation is of critical importance, because it allows to adapt behavior. In a first part of this project, I propose that humans learn to evaluate their own performance based on external feedback. This hypothesis has important consequences, as it provides a mechanistic understanding of how biases in confidence can emerge. In ...
Explainable machine learning, predictive modelling, and causal inference, applied to medical data Hasselt University
Optimal causal learning using electronic health records Ghent University
Routinely collected health data is increasingly used with the aim to
infer the causal effect of a treatment of interest on an outcome. The
eventual goal is to use this knowledge to steer interventions.
However, because treated and untreated patients often have very
different characteristics, any statistical analysis must account for the
confounders that distort the treatment-outcome association. It is ...
BEyOND: Brain pEt to Overcome Neurodegenerative Diseases. KU Leuven
BEyOND trains ESR fellows in innovative approaches to develop quantitative brain PET imaging for better patient care and accelerated drug development for neurodegenerative diseases. Brain disorders are a major public health problem in Europe, and pharmaceutical drugs are the predominant therapeutic approach. In order to manage the constantly increasing healthcare and drug development costs, there is an urgent need for next-generation ...
Evidence Based Simulation Education and Lifelong Learning through ICT 2.0 HOGENT
Explainable AI for student prompting in higher education VIVES
Defining and doing sustainable investing: understanding the European approach. University of Antwerp
Towards precision health by enabling multimodal monitoring in real-life settings using uncertainty based hierarchical and time-dynamic models Ghent University
I will construct a multimodal and dynamic hierarchical sensing framework to tackle the challenges of personalized health monitoring in real-life settings. Multimodal sensing allows me to detect non-physiological symptoms by incorporating context. By fusing behavior modeling with hierarchical anomaly detection using an active learning approach, I will define the optimal moment to gather user feedback for the time dynamic models.