Chris Schwiegelshohn

Chris Schwiegelshohn is an Associate Professor at the Department of Computer Science, Aarhus University, specializing in theoretical computer science and algorithm design. His research focuses on clustering algorithms, coresets, approximation algorithms, and fairness in machine learning. He has contributed significantly to the development of efficient algorithms for large-scale data analysis and privacy-preserving techniques.

Key research interests include optimization in k-means clustering, distributed privacy protocols, and fair recommendation systems. His work bridges theoretical foundations with practical applications in data mining and machine learning.

Publications span topics such as PAC learning for k-means, dynamic facility location, and fair projections for balanced recommendations. He has explored tradeoffs between computational efficiency and accuracy in big data clustering, as well as low-distortion clustering techniques for ordinal data.

No scientific awards or grants are explicitly listed in the provided texts. His advising record remains unspecified.