Background

• Assistant Professor, Data Science, 2022-today, Radboud University, NL
• Postdoc in Data Science, 2021-2022, Radboud University, NL
• PhD in Computational Immunology, 2016-2020, Radboudumc, NL
• MSc in Molecular Mechanisms of Disease, 2014-2016, Radboud University
• BSc in Molecular Life Sciences, 2011-2014, Radboud University
• BSc in Chemistry, 2011-2014, Radboud University

About

In contrast to the popular Dutch saying “meten is weten” (“data equals knowledge”), gathering data is only the first step towards learning something new. This is especially important in (human) biology: important processes like immunology and cancer growth are incredibly complex. Even though technological developments allow us to gather increasing amounts of data on these processes, this does not mean we truly know how they work. And yet, this understanding is crucial: to develop new therapies or to reliably predict which medicine will help a patient most, we need to better understand these processes.

As a researcher and teacher at the interface of computational biology and (applied) data science, I am interested in all methods that can help us learn more (and more reliably) from the increasingly complex datasets that biology has to offer. I therefore work on methods that can translate all these sources of data into usable knowledge of biological systems. I do this through a combination of data-driven research (be it through classical statistics or machine learning) and model-driven research in the form of computer simulations. By building realistic simulations of biological systems–from the biology to the measurement process–I aim to develop new tools that can deal with the specific challenges these datasets offer.

To further maximise the impact of this work, I also invest in interdisciplinary communication and teach students the art of modelling and scientific thinking. Finally, I create open-source software packages such as Artistoo and celltrackR.