I obtained my BSc in Psychology from the University of Groningen, where I focused on research methods from early on. The crisis in replicability in the social sciences has raised my interest in Bayesian methods which soon became my primary focus. I went on to do a minor in theoretical statistics at the Chinese University of Hong Kong and obtained my MSc in Statistics with distinction from Warwick University in 2018. During my masters, I focused on Bayesian methods, classical machine learning methods, as well as, stochastic simulation methods, in particular MCMC. For my master dissertation I 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 my interest in modern Bayesian methods. Having a background both in social as well as mathematical sciences I recognise the value of interdisciplinary learning. My research interests are directed towards modern machine learning methods, which are inspired by real world mechanism that we want to model.
Having a background in statistics I prefer a more theoretical approach to machine learning focusing on model building and formal reasoning. I am funded by Cancer Research UK. Having been in touch with several researchers from the Oxford Cancer Centre, I have gained an insight into some of the exciting research being conducted here. I am excited to get involved more in the future.