Task-Restricted Symmetries in Recurrent Weight Space
S. Dräger. ICML 2026 Workshop on Weight-Space Symmetries: from Foundations to Practical Applications.
Research in machine learning, deep learning, and computational neuroscience.
I am a researcher at the Computational Neurobiology Laboratory (CNL) at the Salk Institute, led by Prof. Terry Sejnowski.
I obtained my undergraduate degree in Computer Science from University of Düsseldorf in 2022 and an M.S. in Computer Science from UC Davis in 2025.
Methods for learning from complex scientific and neurophysiological data.
Neural-network approaches to representation learning and predictive modeling.
Computational methods for studying neural systems and behavior.
S. Dräger. ICML 2026 Workshop on Weight-Space Symmetries: from Foundations to Practical Applications.
X. Xiang, S. Dräger, and J. Zhang. ICASSP 2026.
C. Lainscsek, P. Salami, S. Dräger, S. S. Cash, and T. J. Sejnowski. Accepted to the Proceedings of the National Academy of Sciences. arXiv:2508.16733.
X. Xiang, S. Dräger, and J. Zhang. PRICAI 2025, poster presentation.
S. Dräger and J. Dunkelau. arXiv:2210.16003.
Research software and projects selected for a concise view of current work.