About me

I am a statistical consultant focused on experimental design and other optimal data gathering techniques, particularly for dynamic systems.

Often, researchers will perform experiments and only talk to a statistician afterwards. But statisticians are not only useful for data analysis. Much time and effort can be saved by carefully planning an experiment. Every measurement setup has its own unique details. I help other researchers by constructing optimal experiments, custom made for their own measurement setup, goals, and budgetary constraints.

Much of my expertise in experimental design, I developed during my Research Foundation Flanders (FWO) PhD fellowship at the KULeuven. There, I designed experiments to precisely characterize the respiration and fermentation of fruit and vegetables, leading to better storage solutions.

After graduating, I joined the manufacturing and applied statistics department of Johnson & Johnson to valorize the ideas developed in my PhD. I worked on designing accelerated stability studies to precisely predict the shelf life of vaccines and other drug products. Some of my other responsibilities included designing high-throughput experiments to optimize the manufacturing conditions of chemical reactions, Bayesian mixed effect modelling to determine the mechanical properties of powders, and optimally blocking experiments when full randomization is not an option.

I then felt ready for a new challenge, starting my own consulting company, Strouwen Statistics. My first client is JuliaHub, where I work on the statistical aspects of the quantitative systems pharmacology software, PumasQSP, as well as the battery simulation software, JuliaSimBatteries. I am also responsible for the continuous integration and delivery of the documentation of the Scientific Machine Learning Project (SciML).