In addition to my medical training (MSc, 2006; MD/PhD, 2010), I received degrees in Mathematics and Computer Science (Propaedeutic exam, 1998) and Cognitive Science and Engineering (MSc, 2002). These opened a multidisciplinary toolbox that I use to answer complex research questions; this toolbox includes programming skills, machine learning, statistics, and biomedical knowledge. Early on in my career, I understood that the vast amount of publicly available data constitutes a gold mine. Constructing large-scale datasets from open data and performing advanced analyses can generate valuable new insights often missed by the researchers who originally collected the data. Therefore, I’m highly motivated to maximize and contribute to open data and public tools.
In 2013, I established an independent multidisciplinary research group at the UMCG. The group uses big-data approaches combined with machine learning (ML) to identify molecular, imaging, or clinicopathological patterns relevant to the pathophysiological behavior and treatment response of tumors. I’m known for stimulating students to acquire new skills outside of their ‘comfort zone’, to nurture the next generation of multidisciplinary researchers who can successfully bridge the gap between data science and biomedical research. I’m a team player who can collaborate extensively with clinicians, biologists, and data scientists in medical oncology and beyond.
I followed the residency program in Internal Medicine / Medical Oncology and now work as a board-certified internist and medical oncologist at the department of Medical Oncology at the UMCG, involved in care, teaching, and research. In addition, I am a tenure track adjunct professor in Artificial Intelligence Approaches for Precision Oncology.