Tim van de Brug

Research statement

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 detailed information from images, not visible to the naked eye, and use this for prediction of 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.


Selected publications

MM van Nee, T van de Brug, MA van de Wiel. Fast marginal likelihood estimation of penalties for group-adaptive elastic net. Submitted (2021). link

SD Roosendaal, T van de Brug, S Blaser, A Vanderver, NI Wolf, MS van der Knaap. Imaging patterns characterizing mitochondrial leukoencephalopathies. American Journal of Neuroradiology (2021).

JJ Eertink, T van de Brug, SE Wiegers, GJC Zwezerijnen, EAG Pfaehler, PJ Lugtenburg, B van der Holt, HCW de Vet, OS Hoekstra, R Boellaard, JM Zijlstra. 18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma. Submitted (2020).

MCF Cysouw, BHE Jansen, T van de Brug, DE Oprea-Lager, E Pfaehler, BM de Vries, RJA van Moorselaar, OS Hoekstra, AN Vis, R Boellaard. Machine learning-based analysis of [18F]DCFPyl PET radiomics for risk stratification in primary prostate cancer. European Journal of Nuclear Medicine and Molecular Imaging (2020). link

T van de Brug, F Camia, M Lis. Spin systems from loop soups. Electronic Journal of Probability (2018). link

T van de Brug, F Camia, M Lis. Random walk loop soups and conformal loop ensembles. Probability Theory and Related Fields (2016). link

T van de Brug. Percolation, loop soups and stochastic domination. PhD Thesis Vrije Universiteit Amsterdam (2015). link


Selected talks

Stable prediction with radiomics data, Workshop Statistical Challenges in Medical Data Science, Switzerland (2019)

Random walk loop soups and conformal loop ensembles, Indian Statistical Institute Delhi, India (2016)

Random walk loop soups and conformal loop ensembles, Brazilian School of Probability, Brazil (2014)

Random walk loop soups and conformal loop ensembles, Conference on Stochastic Processes and their Applications, Argentina (2014)

Convergence of the outer boundaries of random walk loop soup clusters to CLE, New York University Abu Dhabi, United Arab Emirates (2014)