Joren Brunekreef

I am a senior postdoctoral researcher at the Netherlands Cancer Institute (NKI) and the University of Amsterdam (UvA), where I am part of Prof. Jan-Jakob Sonke’s radiotherapy department and Dr. Jonas Teuwen’s AI for Oncology lab.

My research focuses on the application of foundation models and self-supervised learning to address open challenges in oncology. I investigate deep learning techniques for the interpretation of high-dimensional biological data, spanning the analysis of histopathology slides, the processing of volumetric CT scans for radiotherapy, and the prediction of protein properties via language model embeddings.

Supported by an NWO AiNed XS grant, I am working to mitigate domain shift, a common issue in AI where models perform well on the data they were trained on but poorly on unfamiliar data. My goal is to develop lightweight adaptation methods that allow for the safe deployment of AI models on data from different scanners or acquisition protocols. This would improve their utility and minimize the need for new annotated datasets.

In parallel to my own research work, I co-supervise several MSc and PhD students in our institute. I also collaborate with various research groups at the NKI on interdisciplinary projects.

I hold a PhD in nonperturbative quantum gravity under the guidance of Prof. Renate Loll at the Radboud University Nijmegen. My work primarily involved investigating curvature observables in the context of Causal Dynamical Triangulations (CDT). During this period, I co-developed open-source codebases for performing Markov chain Monte Carlo simulations of CDT in two and three dimensions.

Additionally, I briefly worked as a junior researcher in the infectious diseases modeling group at the UMC Utrecht’s Julius Center. Collaborating with Dr. Alexandra Teslya and Prof. Mirjam Kretzschmar, I helped develop an agent-based network model for simulating societal lockdowns in response to infectious disease outbreaks.