Dr. Bastian Rieck

Helmholtz Pioneer Campus Principal Investigator (HPC)


Chair of Mathematical Modelling of Biological Systems (Prof. Theis)

Contact Details

Business card at TUMonline

Academic Career and Research Areas

Bastian Rieck received both his M.Sc. degree in mathematics (graduated with distinction) and his Ph.D. ('summa cum laude') in computer science from Heidelberg University in Germany. He subsequently worked as a postdoctoral researcher and later on as a senior assistant at the Machine Learning and Computational Biology Lab of ETH Zurich in Switzerland.

Dr. Rieck (b. 1986) is researching geometrical and topological machine learning methods with applications in biomedicine. Biomedical research commonly observes complex systems at different resolutions, ranging from the macroscopic to the microscopic. Zooming in provides us with the 'fine print' (e.g. individual neurons in a brain), while zooming out lets us see the 'big picture' (e.g. locally-connected networks of neurons, or areas in the brain). For many applications, there is not just one specific scale to consider—relevant features might occur on multiple scales and a priori information about their suitability for a specific task is typically lacking. With noise being an inevitable part of such investigations, we need tools that enable robust multi-scale analyses. Dr. Rieck's research agenda centres on creating, cultivating, and critiquing such tools based on topological machine learning techniques, with a specific focus on healthcare topics.


    - Helmholtz Pioneer Campus Principal Investigator (2021)
    - SNSF Spark Grant for the project "TOPAZ: Topology of Alzheimer's"
    - Ph.D. awarded with "summa cum laude" (2017)
    - Merit scholarship of the National German Science Foundation (2011–2014)

    O'Bray L*, Rieck B*, Borgwardt B: "Filtration Curves for Graph Classification". Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). 2021: 1267–1275.


    Borgwardt K, Ghisu E, Llinares-López F, O'Bray L, Rieck B: "Graph Kernels: State-of-the-Art and Future Challenges". Foundations and Trends® in Machine Learning. 2020; 13 (5–6): 531–712.


    Rieck B*, Yates T*, Bock C, Borgwardt K, Wolf G, Turk-Browne N**, Krishnaswamy S**: "Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence". Advances in Neural Information Processing Systems. 2020; 33: 6900–6912.


    Moor M*, Horn M*, Rieck B**, Borgwardt K**: "Topological Autoencoders". Proceedings of the 37th International Conference on Machine Learning. 2020: 7045–7054.


    Rieck B*, Bock C*, Borgwardt K: "A Persistent Weisfeiler–Lehman Procedure for Graph Classification". Proceedings of the 36th International Conference on Machine Learning. 2019: 5448–5458.