Joren Brunekreef


I am a 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 current research interests include quantifying uncertainty in deep learning and, more concretely, improving the calibration of image segmentation models to enhance their reliability in a medical setting. I approach these challenges through the lens of conformal prediction, a framework offering statistically valid prediction intervals in a model-agnostic manner.

I recently completed 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.

selected publications

  1. PRD
    Curvature profiles for quantum gravity
    Joren Brunekreef, and Renate Loll
    Physical Review D, Jan 2021
  2. arXiv
    Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
    Joren Brunekreef, Eric Marcus, Ray Sheombarsing, and 2 more authors
    Nov 2023
  3. PRD
    On the nature of spatial universes in 3D Lorentzian quantum gravity
    Joren Brunekreef, and Renate Loll
    Physical Review D, Jan 2023
  4. CPC
    Simulating CDT quantum gravity
    Joren Brunekreef, Renate Loll, and Andrzej Görlich
    Computer Physics Communications, Jul 2024