My practical and theoretical background enables me to take an integrative data approach to identify driver genetic alterations, genes, and biological pathways relevant to the pathophysiological behaviour and treatment response of tumours and rapidly translate these findings to pre-clinical experiments and clinical practice. Key aspects of my research are creating large-scale datasets and analysing these with machine learning algorithms capable of distilling complex multifactorial information from big multifactorial data.
In supplementation to my training as a medical oncologist, I completed one year of Mathematics and Computer Science. after which I continued to obtain my MSc degree in Cognitive Science and Engineering (Artificial Intelligence).
In parallel to my medical studies, I worked for several years as a bio-informatician at the Department of Genetics, UMCG. In 2010, I obtained my PhD on high dimensional data analysis for new insights into ovarian cancer phenotypes, after which I continued as an affiliated post-doc at the Departments of Genetics and Medical Oncology (UMCG).
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 with a research group in which I supervise PhD students with various backgrounds: bio-informaticians, clinicians, biostatisticians, machine learners, and biologists.