About

I am a Reader in Mathematics at the University of York working in statistical learning theory and its applications to artificial intelligence. My research combines ideas from empirical process theory and functional analysis to develop rigorous mathematical models for understanding and improving machine learning systems.

I am particularly interested in the statistical and mathematical principles underlying modern machine learning. My recent work studies questions around efficient representations, model compression and kernel methods, with applications to compute-efficient AI.

Recent News

• 2026: Compressed Empirical Measures (in finite dimensions), published in Annales Henri Lebesgue.
• 2026: Senior Area Chair, NeurIPS.
• 2026: Area Chair, ICML.
• 2026: Invited seminar, Université de Versailles.
• 2026: Designed and taught the MAGIC PhD course Mathematical Foundations of AI.
• 2025: Estimating the Mixing Coefficients of Geometrically Ergodic Markov Processes, published in IEEE Transactions on Information Theory.

Selected Publications

• S. Grünewälder, Compressed Empirical Measures (in finite dimensions),  Annales Henri Lebesgue, 2026.
• S. Grünewälder and A. Khaleghi, Estimating the Mixing Coefficients of Geometrically Ergodic Markov Processes, IEEE Transactions on Information Theory, 2025. 
• S. Page and S. Grünewälder,  The Goldenshluger-Lepski Method for Constrained Least-Squares Estimators over RKHSs, Bernoulli, 2021.
• C. Pike-Burke, S. Agrawal, C. Szepesvári and S. Grünewälder, Bandits with Delayed Anonymous Feedback, International Conference on Machine Learning (ICML), 2018.