Cornelius obtained his BSc in Psychology from the University of Groningen, where he focused on research methods from early on. The crisis in replicability in the social sciences has raised his interest in Bayesian methods which soon became his primary focus. He went on to do a minor in theoretical statistics at the Chinese University of Hong Kong and obtained his MSc in Statistics with distinction from Warwick University in 2018. During his masters, he focused on Bayesian methods, classical machine learning methods, as well as, stochastic simulation methods, in particular MCMC. For his master dissertation he looked at high-dimensional heterogeneous socioeconomic and biological data to predict perinatal depression using various machine learning methods. In particular, Bayesian variable selection effectively identified relevant features from a sparse feature space which further fuelled his interest in modern Bayesian methods. Having a background both in social as well as mathematical sciences he recognises the value of interdisciplinary learning.