Principal Investigator Bastian Rieck
Our world is full of phenomena that occur at multiple scales. Biomedical research, for instance, 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. Our research agenda is to create, cultivate, and critique such tools based on topological machine learning techniques, with a specific focus on healthcare topics.
Dr. Bastian Rieck
Principal Investigator, aidos lab
Bastian 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 (senior) postdoctoral researcher at the Machine Learning and Computational Biology Lab of ETH Zurich in Switzerland. Bastian's research primarily focuses on the development of topological machine learning techniques in the context of healthcare applications. He also has a keen interest in developing techniques that improve our understanding of neural networks.
Dr. Bastian Rieck
Positions and Career
2021 - present
Principal Investigator at Helmholtz Pioneer Campus, Helmholtz Zentrum München, Germany
Senior assistant at ETH Zurich, Switzerland
Postdoctoral researcher at ETH Zurich, Switzerland
Researcher at Heidelberg University, Germany
Research assistant at Kaiserslautern University, Germany
Research assistant at Heidelberg University, Germany.
Ph.D. in Computer Science at Heidelberg University, Germany
M.Sc.1 in Mathematics at Heidelberg University, Germany
Honors and Awards
Outstanding reviewer (top 10%) for ICML
Outstanding reviewer (among the top 500 reviewers) for ICLR
Top reviewer for ICML 2020
Outstanding reviewer for the ECML PKDD5 2019 Journal Track
Outstanding reviewer (among the top 400 reviewers) for NeurIPS
Outstanding reviewer (top 5%) for ICML
Outstanding reviewer (among the top 200 reviewers) for NeurIPS
Outstanding reviewer (among the top 100 reviewers) for ICML
Award for the best extended abstract at TopoInVis
Research scholarship, Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp), Heidelberg University, Germany
MSc in Mathematics at Ruprecht-Karls Universität Heidelberg
Research Focus: Robust Topological Representation Learning
Master Thesis: Generalised Intersection Homology Theories
Hobbies: Hiking, Travelling, Festivals, Art
BSc.: Physics at Universität Regensburg
BSc. Thesis: “Fabrication and characterization of patterned constrictions in few-layer NbSe2 crystals"
Research Interest: Modelling air pollution through machine learning
Hobbies: Hiking, Music, Reading, Cooking
Back to the basics with inclusion of clinical domain knowledge — A simple, scalable, and effective model of Alzheimer’s Disease classification.
S. C. Brüningk†, F. Hensel†, L. Lukas, M. Kuijs, C. R. Jutzeler‡, and B. Rieck‡
Proceedings of the 6th Machine Learning for Healthcare Conference. Proceedings of Machine Learning Research, 2021. In press.
M. Moor†, B. Rieck†, M. Horn, C. R. Jutzeler‡, and K. Borgwardt‡
in Medicine 8, 2021. doi: 10.3389/fmed.2021.607952.
R. Vandaele, B. Rieck, Y. Saeys, and T. De Bie
Pattern Recognition Letters 147, pp. 85–92, 2021. doi: 10.1016/j.patrec.2021.03.035
B. Rieck†, T. Yates†, C. Bock, K. Borgwardt, G. Wolf, N. Turk-Browne‡, and S. Krishnaswamy‡
Advances in Neural Information Processing Systems (NeurIPS), Volume 33, pp. 6900–6912, 2020. Accepted as a spotlight presentation at NeurIPS (top 3% of all submissions). arXiv: 2006.07882 [q-bio.NC]
K. Borgwardt, E. Ghisu, F. Llinares-López, L. O’Bray, and B. Rieck
Foundations and Trends® in Machine Learning 13:5–6, pp. 531–712, 2020. doi: 10.1561/2200000076. arXiv: 2011.03854 [cs.LG]
Helmholtz Pioneer Campus
Helmholtz Zentrum München
Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
Ingolstädter Landstr. 1