Tim van de Brug
My research focuses on high-dimensional statistics and machine learning for the analysis of medical imaging data. I specialize in both MRI and PET data, with applications in neuroscience and oncology. I develop advanced methodology to extract very detailed information from images, not visible to the naked eye, and use this to predict disease progression and treatment response. For example, I am currently working on a radiomics approach to select the optimal therapy for diffuse large B-cell lymphoma patients based on whole-body PET-CT scans. My background is in mathematics, in particular probability theory and mathematical physics.
T. Klausch, P.M. van de Ven, T. van de Brug, M.A. van de Wiel, and J. Berkhof, Estimating Bayesian optimal treatment regimes for dichotomous outcomes using observational data, Submitted (2018). link
T. van de Brug, F. Camia, and M. Lis, Spin systems from loop soups, Electronic Journal of Probability 23 (2018), no. 81, 1-17. link
T. van de Brug, F. Camia, and M. Lis, Random walk loop soups and conformal loop ensembles, Probability Theory and Related Fields 166 (2016), no. 1, 553-584. link
T. van de Brug, Percolation, loop soups and stochastic domination, PhD Thesis Vrije Universiteit Amsterdam (2015). link