I am a PhD student in the field of Theoretical Epidemiology and Biostatistics at the Julius Center for Health Sciences and Primary Care in Utrecht. I obtained my Master’s degree in Artificial Intelligence in 2009 and am currently a postgraduate student in Clinical Epidemiology. My experiences are mainly founded in the field of Prediction Research, Machine Learning, Statistics and Informatics. More specific, I have worked with classification and regression techniques, numerical optimization methods, evolutionary algorithms, signal processing, intelligent search techniques, multi-agent systems, but also on more technical issues such as computer networks, programming, multithreading, generics and remote procedure calls. At this time, I investigate methods to aggregate previously published clinical prediction models with newly collected individual patient data. These methods rely on robust estimation techniques and aim to improve the generalization of novel prediction models. My master thesis studies the classification task in the presence of class imbalanced data, e.g. diagnosing diseases with a low prevalence. Its main contribution is the introduction of a new approach which outperforms a number of established approaches when dealing with small and class imbalanced datasets.