Anders Gjølbye

Anders Gjølbye Madsen is a PhD fellow in machine learning for health at the Technical University of Denmark and currently a visiting researcher at Stanford. His research focuses on making machine learning models more reliable and understandable in medical settings, especially for neurological diagnosis using EEG and other brain-signal data. He has published work at NeurIPS on improving explanations of nonlinear models, as well as papers on EEG preprocessing and interpretability for clinical machine learning. Before starting his PhD, Anders worked on machine learning methods for portable epilepsy diagnostics, which shaped his interest in models that can work outside carefully controlled research settings.