About

My background lies in the field of theoretical physics. In particular, I obtained my Ph.D. in fundamental research on string theory and black holes. Afterward, I decided to pursue research problems with a more immediate application. In that spirit, I did an internship in neurodiagnostics at AMC, where I sought to classify subtypes of Alzheimer's in pathology slides using artificial intelligence methods.

At the NKI I am continuing my interests in deep learning and medical imaging, investigating methodological aspects and applications of, for example, self-supervised learning and geometrical deep learning.

Last 5 publications

1. E. Marcus, R. Sheombarsing, J. Sonke, J. Teuwen, "Constrained Empirical Risk Minimization: Theory and Practice", 2023
2. B. P. Y. Kwee, M. Messemaker, E. Marcus, G. Oliveira, W. Scheper, C. J. Wu, J. Teuwen, T. N. Schumacher, "STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data", 2023
3. R. Lo Gullo, E. Marcus, J. Huayanay, S. Eskreis-Winkler, S. Thakur, J. Teuwen, K. Pinker, "Artificial Intelligence-Enhanced Breast MRI", Investigative Radiology, 2023
4. U. Gürsoy, D. Kharzeev, E. Marcus, K. Rajagopal, C. Shen, "Charge-dependent flow induced by electromagnetic fields in heavy ion collisions", Nuclear Physics A, 2021, 1005
5. C. Couzens, E. Marcus, K. Stemerdink, D. Van De Heisteeg, "The near-horizon geometry of supersymmetric rotating AdS4 black holes in M-theory", Journal of High Energy Physics, 2021, 2021;(5)