I enjoy discovering how the world works. As a physicist I have learned to capture nature in mathematical models. In a multidisciplinary environment, I have been applying this approach to human data, specifically for understanding the relationships between human brain and behavior in health and disease. Starting as postdoc in the UMCU neuroimaging group, I set up an image-processing pipeline for quantitative analysis of thousands of MRI brain images. As assistant professor, I implemented advanced statistical analyses to study dynamic changes in brain morphology. Central in my research have always been how (image) data represents information and how knowledge of data quality can be used to perform optimal analyses. Applied to multicenter imaging studies, this work resulted in a method to determine reliability of, for example, (twin) heritability or longitudinal studies. At that time the first machine learning steps in this field were made, and I shifted my focus from group-level analyses to making predictions about individuals, based on their data. My team performed the first large-scale study to classify 
individuals with and without schizophrenia based on MRI brain images. I have expanded the use of pattern recognition analyses to applications in other domains, including clinical data and vocabulary data. 
Currently, my research focuses on the development and use of innovative analytical approaches, including machine learning, for making models to describe (the development and heterogeneity of) brain, language and behavior. This work includes individualized prediction of (risk of) mental and developmental disorders and illness course (Psychosis Prognosis Predictor; ePODIUM: early prediction of developmental dyslexia in infants using machine learning).