Task-Restricted Symmetries in Recurrent Weight Space
S. Dräger. ICML 2026 Workshop on Weight-Space Symmetries: from Foundations to Practical Applications.
Simon Dräger
sfdraeger at gmail.comResearch in machine learning, deep learning, and computational neuroscience.
About
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.
Research focus
Machine learning
Methods for learning from complex scientific and neurophysiological data.
Deep learning
Neural-network approaches to representation learning and predictive modeling.
Computational neuroscience
Computational methods for studying neural systems and behavior.
Publications
3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion
X. Xiang, S. Dräger, and J. Zhang. ICASSP 2026.
Causal Dynamic Resonance
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.
EffiPerception: A Plug-and-Play Efficiency Enhancement Framework for 2D and 3D Perception Models
X. Xiang, S. Dräger, and J. Zhang. PRICAI 2025, poster presentation.
Evaluating the Impact of Loss Function Variation in Deep Learning for Classification
S. Dräger and J. Dunkelau. arXiv:2210.16003.
Selected research
Research software and projects selected for a concise view of current work.