Faculty
Dr. Rebekka Burkholz is a tenured faculty at the CISPA Helmholtz Center for Information Security in Saarbrücken. The focus of her research is relational machine learning. Her main goal is to gain a theoretical understanding of deep learning from a complex network perspective and improve contemporary algorithms based on these insights. Her favourite applications are currently in molecular biology.
From 2019-2021, she was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health working with John Quackenbush. Before that, she enjoyed postdoctoral research at ETH Zurich, from 2017-2018 at the Institute for Machine Learning with Joachim Buhmann and from 2016-2017 at the Chair of Systems Design with Frank Schweitzer. Her PhD research from 2013-2016 at the ETH Risk Center was supervised by Frank Schweitzer and co-supervised by Hans J. Herrmann. Her thesis on systemic risk won the Zurich Dissertation Prize and her work on international maize trade received the CSF Best Contribution Award. She studied Mathematics and Physics at TU Darmstadt.
International Conference on Machine Learning Workshop(ICML- W)
HORST: Composing Optimizer Geometries for Sparse Transformer Training
International Conference on Machine Learning workshop(ICML-W)
Prune to Protect: Faster Training and Enhanced Privacy by Dynamic Data Pruning
International Conference on Machine Learning Workshop (ICML-W)
Don't Trust Stubborn Neighbors: A Security Framework for Agentic Networks
International Conference on Machine Learning Workshop (ICML-W)
Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?
workshop on international Conference on Machine Learning(ICML-W)
Linear GCNs Need Better Bias, Not More Expressive Power
International Conference on Machine Learning workshop (ICML-W)
Continuous Sparsification via Minimizing Movement
International Conference on Machine Learning (ICML)
Fixed Aggregation Features Can Rival GNNs
International Conference on Machine Learning (ICML)
International Conference on Machine Learning (ICML)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Bridging Domains through Subspace-Aware Model Merging