Statistician • Data Scientist • Sociologist
PhD Candidate in Methods and Statistics
I am a PhD candidate at the Methods and Statistics department of Utrecht University, researching different techniques for creating privacy-preserving synthetic data sets, under the supervision of Dr. Erik-Jan van Kesteren, Dr. Peter-Paul de Wolf and Prof. Dr. Stef van Buuren. I aim to work at the intersection of social-scientific research and cutting-edge statistical techniques, to get the most out of expensively collected research data.
In the past, I worked on several projects on evidence synthesis, aiming to aggregate evidence over heterogeneous studies that do not allow for meta-analysis. Together with Irene Klugkist I outlined and evaluated the methodology, while I applied it on a set of heterogeneous, sociological studies with Vincent Buskens and Werner Raub. Additionally, I worked on several projects in a broad range of topics (multiple imputation of missing data, unsupervised text analysis and hypothesis evaluation using information criteria).
Besides research, I teach graduate and post-graduate level courses in data science techniques and multiple imputation of missing data.
MSc in Methods and Statistics for the Behavioural, Biomedical and Social Sciences, 2022
Utrecht University
MSc in Sociology and Social Research, 2022
Utrecht University
BA in Liberal Arts & Sciences, 2019
Utrecht University
Over the years, I have teached in courses with topics ranging from structural equation modeling and missing data methods to social network analysis.
My research concentrates on developing and advancing methodology to create and evaluate privacy-preserving synthetic data, that aims to overcome disclosure risks related to disseminating research data.
Over the past years, I consulted in multiple projects with applied researchers (ranging from sociologists and educational scientists to medical scientists). Drop me a line if you are interested.
Research Master’s programme
Research Master’s programme
Bayesian Evidence Synthesis is a method to integrate the results of multiple studies with varying, seemingly incompatible, designs using Bayes Factors, to enhance the aggregation of scientific evidence.
Synthetic data allows for openly sharing of research data, without disclosing identifying information of the participants, that could be as informative as the actually observed data.