About

My scientific interest lies in the (computational) modelling of socio-economic systems in order to improve our understanding of the often complex societal issues we face in society. For this I use tools from complexity science, such as agent-based modelling, network science and machine learning. Recently, I focus most on simulation-based inference in (complex) socio-economic systems, with an emphasis on emprical model calibration and policy simulations. Research in this field has now started to combine both computational modelling and machine learning techniques, each of which, in my view, can complement each other. Machine learning methods can provide efficient sampling techniques and/or act as surrogates for computationally expensive models, yet they are often black-box techniques and cannot provide generative explanations (yet), for emergent phenomena in socio-economic systems. Hence, the generative explanations coming from computational models such as ABM, combined with the efficiency of machine learning techniques, have the potential to improve our understanding of complex social systems and hopefully reduce inequalities that are often found within them. The main focus of my PhD is to model primary school choice and resulting patterns of school segregation using Agent-Based Models (ABM), as well as analysing potential policy disruptions. Besides science, I climb, run, love sports in general, drink beers and like roadtrips.