Rebekka Burkholz

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Her work bridges relational machine learning with biological applications, focusing on theoretical improvements to deep learning through complex network analysis.

• Education: Studied Mathematics and Physics at TU Darmstadt, followed by a PhD at ETH Risk Center under Frank Schweitzer and Hans J. Herrmann.
• Postdoctoral Experience: Worked at Harvard's Biostatistics Department (2019-2021) and ETH Zurich's Machine Learning Institute (2017-2019).

Her research explores graph neural networks, sparse training, and biologically informed models, aiming to enhance algorithm robustness and interpretability. Recent publications analyze:

• 2025: Implicit sparsification, hyperbolic regularization, and network balance techniques.
• 2024: Spectral pruning, gene regulatory modeling, and dynamic rescaling for GNNs.

Scientific recognition includes:

• Zurich Dissertation Prize for systemic risk research (2016).
• CSF Best Contribution Award for maize trade analysis (2016).

She collaborates with institutions like Harvard T.H. Chan School of Public Health and ETH Zurich, focusing on trustworthy information processing and empirical security at CISPA.