Machine Learning & Optimization Lab

We are a research group at the CISPA Helmholtz Center for Information Security in Saarbrücken and St. Ingbert, Germany.

We develop foundations and algorithms for modern machine learning and optimization, with a focus on training and updating models efficiently and reliably at scale. Our work spans modern optimization methods (including decentralized and non-convex optimization), communication-efficient and privacy-aware learning (federated and decentralized training), and learning under heterogeneity and distribution shift, including continual learning, personalization, and unlearning. We also study how models can share knowledge across sites and even across architectures to enable long-running, collaborative learning systems.

Methodologically, we connect mathematical understanding with algorithm design and careful experiments. We especially like problems where real constraints like limited communication, privacy, and changing data lead to clean research questions and practical impact.

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