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.
I am now part of the Intelligent Systems lab at the Department of Computer Science and working under the supervision of Thomas Lukasiewicz. My DPhil is fully funded by Cancer Research UK.
My interests are medical imaging, Bayesian methods in deep learning, robustness, as well as, uncertainty in deep learning and its value in medical applications.