Research
My research centres on how to understand complex data sets, mostly in biomedical contexts, using (among other things) topological machine learning methods. I consider myself to be a toolsmith, creating new tools that help us deal with challenging problems.
If you are interested in topological machine learning, consider joining TDA in ML, a Slack community dedicated to discussing all things topological machine learning.
Contents
Publications
Please find a list of my preprints and publications below. Equal contributions by several authors are indicated using a superscript dagger symbol, i.e. †. A superscript ‘double dagger’, or diesis, i.e. ‡, denotes a publication that was jointly directed. I aim to provide a BibTeX file for citing the publication as well as the slides corresponding to the paper. If neither a preprint nor slides are available but you still want to read the publication, please drop me a line to bastian@rieck.me—it is possible that I am only allowed to share a publication privately.
Preprints
- C. Coupette†, J. Wayland†, E. Simons, and B. Rieck: No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets, Preprint, 2025
[Preprint] • [BibTeX] - B. Rieck: Topology Meets Machine Learning: An Introduction Using the Euler Characteristic Transform, Preprint, 2024
[Preprint] • [BibTeX] - I. Cannistraci, E. Rodolà, and B. Rieck: Detecting and Approximating Redundant Computational Blocks in Neural Networks, Preprint, 2024
[Preprint] • [BibTeX] - E. Röell and B. Rieck: Generative Topology for Shape Synthesis, Preprint, 2024
[Preprint] • [BibTeX] - J. von Rohrscheidt and B. Rieck: Diss-L-Ect: Dissecting Graph Data With Local Euler Characteristic Transforms, Preprint, 2024
[Preprint] • [BibTeX] - K. Limbeck and B. Rieck: Detecting Spatial Dependence in Transcriptomics Data Using Vectorised Persistence Diagrams, Preprint, 2024
[Preprint] • [BibTeX] - J. Wayland, R. J. Funk, and B. Rieck: Characterizing Physician Referral Networks With Ricci Curvature, Preprint, 2024
[Preprint] • [BibTeX] - S. Kališnik, B. Rieck, and A. Žegarac: Persistent Homology via Ellipsoids, Preprint, 2024
[Preprint] • [BibTeX] - S. Kazeminia, C. Marr‡, and B. Rieck‡: Topologically Regularized Multiple Instance Learning to Harness Data Scarcity, Preprint, 2024
[Preprint] • [BibTeX] - B. Holmgren, E. Quist, J. Schupbach, B. T. Fasy, and B. Rieck: The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning, Preprint, 2024
[Preprint] • [BibTeX] - F. Srambical and B. Rieck: Filtration Surfaces for Dynamic Graph Classification, Preprint, 2023
[Preprint] • [BibTeX] - B. Rieck and C. Coupette: Evaluating the “Learning on Graphs” Conference Experience, Preprint, 2023
[Preprint] • [BibTeX] - D. Bhaskar†, J. Moore†, F. Gao, B. Rieck, F. Khasawneh, E. Munch, V. Greco‡, and S. Krishnaswamy‡: Capturing Spatiotemporal Signaling Patterns in Cellular Data With Geometric Scattering Trajectory Homology, Preprint, 2023
[Preprint] • [BibTeX] - C. Weis†, B. Rieck†, S. Balzer†, A. Cuénod, A. Egli, and K. Borgwardt: Improved MALDI-TOF MS Based Antimicrobial Resistance Prediction Through Hierarchical Stratification, Preprint, 2022
[Preprint] • [BibTeX] - B. Rieck: Basic Analysis of Bin-Packing Heuristics, Preprint, 2021
[Preprint] • [GitHub] • [BibTeX] - M. Moor, M. Horn, C. Bock, K. Borgwardt, and B. Rieck: Path Imputation Strategies for Signature Models of Irregular Time Series, Preprint, 2020
[Preprint] • [BibTeX]
A preliminary version of this work was accepted for presentation at the ICML Workshop on the Art of Learning with Missing Values (ARTEMISS)
2025
- L. Hetzel†, J. Sommer†, B. Rieck, F. Theis, and S. Günnemann: MAGNet: Motif-Agnostic Generation of Molecules From Shapes, International Conference on Learning Representations, 2025 (in press)
[Preprint] • [BibTeX] - Y. Zhang, L. Mezrag, X. Sun, C. Xu, K. Macdonald, D. Bhaskar, S. Krishnaswamy‡, G. Wolf‡, and B. Rieck‡: Principal Curvatures Estimation With Applications to Single Cell Data, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025 (in press)
[Preprint] • [BibTeX]
2024
- R. Ballester†, E. Röell†, D. Bin Schmid†, M. Alain†, S. Escalera, C. Casacuberta, and B. Rieck: MANTRA: The Manifold Triangulations Assemblage, International Conference on Learning Representations, 2024 (in press)
[Preprint] • [BibTeX] - D. Buffelli†, F. Soleymani†, and B. Rieck: CliquePH: Higher-Order Information for Graph Neural Networks Through Persistent Homology on Clique Graphs, Proceedings of the Third Learning on Graphs Conference, 2024 (in press)
[Preprint] • [BibTeX] - J. von Rohrscheidt, B. Rieck‡, and S. M. Schmon‡: Bayesian Computation Meets Topology, Transactions on Machine Learning Research, 2024
[BibTeX] - M. Alain, S. Takao, B. Rieck, X. Dong, and E. Noutahi: Graph Classification Gaussian Processes via Hodgelet Spectral Features, ‘Bayesian Decision-Making and Uncertainty’ Workshop at NeurIPS, 2024
[BibTeX] - R. Ballester and B. Rieck: On the Expressivity of Persistent Homology in Graph Learning, Proceedings of the Third Learning on Graphs Conference, 2024 (in press)
[Preprint] • [BibTeX] - K. Limbeck, R. Andreeva, R. Sarkar, and B. Rieck: Metric Space Magnitude for Evaluating the Diversity of Latent Representations, Advances in Neural Information Processing Systems, Volume 37, pp. 123911–123953, 2024
[Preprint] • [BibTeX] - T. Papamarkou, T. Birdal, M. Bronstein, G. Carlsson, J. Curry, Y. Gao, M. Hajij, R. Kwitt, P. Liò, P. Di Lorenzo, V. Maroulas, N. Miolane, F. Nasrin, K. N. Ramamurthy, B. Rieck, S. Scardapane, M. T. Schaub, P. Veličković, B. Wang, Y. Wang, G. Wei, and G. Zamzmi: Position: Topological Deep Learning Is the New Frontier for Relational Learning, Proceedings of the 41st International Conference on Machine Learning, Number 235, pp. 39529–39555, 2024
[Preprint] • [BibTeX] - J. Wayland, C. Coupette‡, and B. Rieck‡: Mapping the Multiverse of Latent Representations, Proceedings of the 41st International Conference on Machine Learning, Number 235, pp. 52372–52402, 2024
[Preprint] • [GitHub] • [BibTeX] - M. F. Adamer, E. De Brouwer, L. O’Bray, and B. Rieck: The Magnitude Vector of Images, Journal of Applied and Computational Topology, Volume 8, Number 3, pp. 447–473, 2024
[Preprint] • [BibTeX] - Y. Zhang†, L. Mezrag†, X. Sun, C. Xu, S. Krishnaswamy‡, G. Wolf‡, and B. Rieck‡: Adaptative Local PCA for Curvature Estimation on Data Manifolds, Helmholtz AI Conference: AI for Science, 2024
[BibTeX] - C. Bock†, J. E. Walter†, B. Rieck†, I. Strebel, K. Rumora, I. Schaefer, M. J. Zellweger, K. Borgwardt‡, and C. Müller‡: Enhancing the Diagnosis of Functionally Relevant Coronary Artery Disease With Machine Learning, Nature Communications, Volume 15, Number 1, 2024
[BibTeX] - R. M. Levenson, Y. Singh, B. Rieck, Q. A. Hathaway, C. Farrelly, J. Rozenblit, P. Prasanna, B. Erickson, A. Choudhary, G. Carlsson, and D. Deepa: Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology, Laboratory Investigation, Volume 104, Number 6, 2024
[BibTeX] - C. Coupette, J. Vreeken, and B. Rieck: All the World’s a (Hyper)Graph: A Data Drama, Digital Scholarship in the Humanities, Volume 39, Number 1, pp. 74–96, 2024
[Preprint] • [Author’s copy] • [GitHub] • [BibTeX] - K. Maggs, C. Hacker, and B. Rieck: Simplicial Representation Learning With Neural $k$-Forms, International Conference on Learning Representations, 2024
[Preprint] • [GitHub] • [BibTeX] - E. Röell and B. Rieck: Differentiable Euler Characteristic Transforms for Shape Classification, International Conference on Learning Representations, 2024
[Preprint] • [GitHub] • [BibTeX]
2023
- C. Morris, Y. Lipman, H. Maron, B. Rieck, N. M. Kriege, M. Grohe, M. Fey, and K. Borgwardt: Weisfeiler and Leman Go Machine Learning: The Story So Far, Journal of Machine Learning Research, Volume 24, Number 333, pp. 1–59, 2023
[Preprint] • [BibTeX] - J. Southern†, J. Wayland†, M. Bronstein, and B. Rieck: Curvature Filtrations for Graph Generative Model Evaluation, Advances in Neural Information Processing Systems, Volume 36, pp. 63036–63061, 2023
[Preprint] • [BibTeX] - H. Alhoori, E. A. Fox, I. Frommholz, H. Liu, C. Coupette, B. Rieck, Ghosal, and J. Wu: Who Can Submit an Excellent Review for This Manuscript in the Next 30 Days? — Peer Reviewing in the Age of Overload, ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 319–320, 2023
[BibTeX] - D. J. Waibel, E. Röell, B. Rieck‡, R. Giryes‡, and C. Marr‡: A Diffusion Model Predicts 3D Shapes From 2D Microscopy Images, IEEE International Symposium on Biomedical Imaging (ISBI), 2023
[Preprint] • [BibTeX] - M. Moor†, N. Bennet†, D. Plecko†, M. Horn†, B. Rieck, N. Meinshausen, P. Bühlmann, and K. Borgwardt: Predicting Sepsis Using Deep Learning Across International Sites: A Retrospective Development and Validation Study, eClinicalMedicine, Volume 62, pp. 102124, 2023
[Preprint] • [BibTeX] - R. Andreeva, K. Limbeck, B. Rieck‡, and R. Sarkar‡: Metric Space Magnitude and Generalisation in Neural Networks, Proceedings of the 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML), Number 221, pp. 242–253, 2023
[Preprint] • [BibTeX] - J. von Rohrscheidt and B. Rieck: Topological Singularity Detection at Multiple Scales, Proceedings of the 40th International Conference on Machine Learning, Number 202, pp. 35175–35197, 2023
[Preprint] • [GitHub] • [BibTeX] - G. Huguet†, A. Tong†, B. Rieck†, J. Huang†, M. Kuchroo, M. Hirn‡, G. Wolf‡, and S. Krishnaswamy‡: Time-Inhomogeneous Diffusion Geometry and Topology, SIAM Journal on Mathematics of Data Science, Volume 5, Number 2, pp. 346–372, 2023
[Preprint] • [BibTeX] - M. Kuchroo†, M. DiStasio†, E. Song, E. Calapkulu, L. Zhang, M. Ige, A. H. Sheth, A. Majdoubi, M. Menon, A. Tong, A. Godavarthi, Y. Xing, S. Gigante, H. Steach, J. Huang, G. Huguet, J. Narain, K. You, G. Mourgkos, R. M. Dhodapkar, M. J. Hirn, B. Rieck, G. Wolf, S. Krishnaswamy‡, and B. P. Hafler‡: Single-Cell Analysis Reveals Inflammatory Interactions Driving Macular Degeneration, Nature Communications, Volume 14, Number 1, pp. 2589, 2023
[BibTeX] - J. L. Moore†, D. Bhaskar†, F. Gao†, C. Matte-Martone, S. Du, E. Lathrop, S. Ganesan, L. Shao, R. Norris, N. C. Sanz, K. Annusver, M. Kasper, A. Cox, C. Hendry, B. Rieck, S. Krishnaswamy‡, and V. Greco‡: Cell Cycle Controls Long-Range Calcium Signaling in the Regenerating Epidermis, Journal of Cell Biology, Volume 222, Number 7, pp. e202302095, 2023
[BibTeX] - B. Giunti, J. Lazovskis, and B. Rieck: DONUT: Creation, Development, and Opportunities of a Database, Notices of the American Mathematical Society, Volume 70, Number 10, pp. 1640–1644, 2023
[Preprint] • [BibTeX] - D. Yoneoka and B. Rieck: A Note on Cherry-Picking in Meta-Analyses, Entropy, Volume 25, Number 4, 2023
[BibTeX] - K. V. Nadimpalli, A. Chattopadhyay‡, and B. Rieck‡: Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images From Single 2D Slices, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 571–579, 2023
[Preprint] • [BibTeX] - C. Coupette, S. Dalleiger, and B. Rieck: Ollivier–Ricci Curvature for Hypergraphs: A Unified Framework, International Conference on Learning Representations, 2023
[Preprint] • [BibTeX]
2022
- D. Thomas, S. Demers, S. Krishnaswamy‡, and B. Rieck‡: Topological Jet Tagging, ‘Machine Learning and the Physical Sciences’ Workshop at NeurIPS, 2022
[BibTeX] - J. Dyer, J. Fitzgerald, B. Rieck, and S. M. Schmon: Approximate Bayesian Computation for Panel Data With Signature Maximum Mean Discrepancies, ‘Temporal Graph Learning’ Workshop at NeurIPS, 2022
[BibTeX] - F. Graf, S. Zeng, B. Rieck, M. Niethammer, and R. Kwitt: On Measuring Excess Capacity in Neural Networks, Advances in Neural Information Processing Systems, Volume 35, pp. 10164–10178, 2022
[Preprint] • [BibTeX] - R. Liu†, S. Cantürk†, F. Wenkel, D. Sandfelder, D. Kreuzer, A. Little, S. McGuire, L. O’Bray, M. Perlmutter‡, B. Rieck‡, M. Hirn‡, G. Wolf‡, and L. Rampášek†‡: Taxonomy of Benchmarks in Graph Representation Learning, Proceedings of the First Learning on Graphs Conference, Number 198, pp. 6:1–6:25, 2022
[Preprint] • [BibTeX]
Accepted as an oral presentation at LoG (top 5% of all submissions) - C. Hacker and B. Rieck: On the Surprising Behaviour of node2vec, Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, Number 196, pp. 142–151, 2022
[Preprint] • [GitHub] • [BibTeX] - D. Bhaskar†, K. MacDonald†, O. Fasina, D. Thomas, B. Rieck, I. Adelstein‡, and S. Krishnaswamy‡: Diffusion Curvature for Estimating Local Curvature in High Dimensional Data, Advances in Neural Information Processing Systems, 2022
[Preprint] • [BibTeX] - K. MacDonald, J. Paige, D. Thomas, S. Zhao, K. You, I. M. Adelstein, Y. Aizenbud, B. Rieck, D. Bhaskar, and S. Krishnaswamy: Diffusion-Based Methods for Estimating Curvature in Data, ‘Geometrical and Topological Representation Learning’ Workshop at ICLR, 2022
[BibTeX] - D. J. Waibel, S. Atwell, M. Meier, C. Marr, and B. Rieck: Capturing Shape Information With Multi-Scale Topological Loss Terms for 3D Reconstruction, Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 150–159, 2022
[Preprint] • [GitHub] • [BibTeX] - L. O’Bray†, M. Horn†, B. Rieck‡, and K. Borgwardt‡: Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions, International Conference on Learning Representations, 2022
[Preprint] • [GitHub] • [BibTeX]
Accepted as a spotlight presentation (top 5% of all submissions) - M. Horn†, E. De Brouwer†, M. Moor, Y. Moreau, B. Rieck‡, and K. Borgwardt‡: Topological Graph Neural Networks, International Conference on Learning Representations, 2022
[Preprint] • [GitHub] • [BibTeX] - S. Horoi†, J. Huang†, B. Rieck, G. Lajoie, G. Wolf‡, and S. Krishnaswamy‡: Exploring the Geometry and Topology of Neural Network Loss Landscapes, Advances in Intelligent Data Analysis XX, pp. 171–184, 2022
[Preprint] • [BibTeX] - C. Weis, A. Cuénod, B. Rieck, O. Dubuis, S. Graf, C. Lang, M. Oberle, M. Brackmann, K. K. Søgaard, M. Osthoff, K. Borgwardt‡, and A. Egli‡: Direct Antimicrobial Resistance Prediction From Clinical MALDI-TOF Mass Spectra Using Machine Learning, Nature Medicine, Volume 28, Number 1, pp. 164–174, 2022
[Preprint] • [GitHub] • [BibTeX] - M. Kuchroo†, J. Huang†, P. Wong†, J. Grenier, D. Shung, A. Tong, C. Lucas, J. Klein, D. B. Burkhardt, S. Gigante, A. Godavarthi, B. Rieck, B. Israelow, M. Simonov, T. Mao, J. E. Oh, J. Silva, T. Takahashi, C. D. Odio, A. Casanovas-Massana, J. Fournier, Y. I. Team, S. Farhadian, C. S. Dela Cruz, A. I. Ko, M. J. Hirn, F. P. Wilson‡, J. G. Hussin‡, G. Wolf‡, A. Iwasaki‡, and S. Krishnaswamy: Multiscale PHATE Identifies Multimodal Signatures Of
COVID-19, Nature Biotechnology, Volume 40, Number 5, pp. 681–691, 2022
[Preprint] • [BibTeX]
2021
- M. Kuijs, C. R. Jutzeler, B. Rieck, and S. C. Brüningk: Interpretability Aware Model Training to Improve Robustness Against Out-of-Distribution Magnetic Resonance Images in Alzheimer’s Disease Classification, ‘Machine Learning for Health (ML4H)’ Symposium, 2021
[Preprint] • [BibTeX] - R. Liu†, S. Cantürk†, F. Wenkel, D. Sandfelder, D. Kreuzer, A. Little, S. McGuire, M. Perlmutter, L. O’Bray, B. Rieck, M. Hirn, G. Wolf, and L. Rampášek: Towards a Taxonomy of Graph Learning Datasets, ‘Data-Centric AI’ Workshop at NeurIPS, 2021
[Preprint] • [BibTeX] - S. Horoi†, J. Huang†, B. Rieck, G. Lajoie, G. Wolf‡, and S. Krishnaswamy‡: Exploring the Loss Landscape of Neural Networks With Manifold Learning and Topological Data Analysis, Montreal AI Symposium, 2021
[BibTeX] - M. D. Lücken†, D. B. Burkhardt†, R. Cannoodt†, C. Lance†, A. Agrawal, H. Aliee, A. T. Chen, L. Deconinck, A. M. Detweiler, A. A. Granados, S. Huynh, L. Isacco, Y. J. Kim, B. De Kumar, S. Kuppasani, H. Lickert, A. McGeever, J. C. Melgarejo, H. Mekonen, M. Morri, M. Müller, N. Neff, S. Paul, B. Rieck, K. Schneider, S. Steelman, M. Sterr, D. J. Treacy, A. Tong, A. Villani, G. Wang, J. Yan, C. Zhang, A. O. Pisco‡, S. Krishnaswamy‡, F. J. Theis‡, and J. M. Bloom‡: A Sandbox for Prediction and Integration of DNA, RNA, and Proteins in Single Cells, Advances in Neural Information Processing
Systems (Datasets and Benchmarks Track), 2021
[BibTeX] - M. Horn†, E. De Brouwer†, M. Moor, Y. Moreau, B. Rieck‡, and K. Borgwardt‡: Topological Graph Neural Networks, 29th Fall Workshop on Computational Geometry, 2021
[BibTeX] - L. O’Bray†, B. Rieck†, and K. Borgwardt: Filtration Curves for Graph Representation, Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1267–1275, 2021
[Author’s copy] • [GitHub] • [BibTeX] - K. Ghalamkari, M. Sugiyama, L. O’Bray, B. Rieck, and K. Borgwardt: Advances in Graph Kernels, Journal of the Japanese Society for Artificial Intelligence, Volume 36, Number 4, pp. 421–429, 2021
[GitHub] • [BibTeX]
This article constitutes an abridged translation of our survey ‘Graph Kernels: State-of-the-Art and Future Challenges’ - S. C. Brüningk†, F. Hensel†, L. Lukas, M. Kuijs, C. R. Jutzeler‡, and B. Rieck‡: Back to the Basics With Inclusion of Clinical Domain Knowledge — A Simple, Scalable, and Effective Model of Alzheimer’s Disease Classification, Proceedings of the 6th Machine Learning for Healthcare Conference, Number 149, pp. 730–754, 2021
[BibTeX] - R. Vandaele, B. Rieck, Y. Saeys, and T. De Bie: Stable Topological Signatures for Metric Trees Through Graph Approximations, Pattern Recognition Letters, Volume 147, pp. 85–92, 2021
[BibTeX] - M. Moor†, B. Rieck†, M. Horn, C. R. Jutzeler‡, and K. Borgwardt‡: Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review, Frontiers in Medicine, Volume 8, 2021
[BibTeX] - F. Hensel, M. Moor, and B. Rieck: A Survey of Topological Machine Learning Methods, Frontiers in Artificial Intelligence, Volume 4, 2021
[BibTeX] - F. Gao, J. Moore, B. Rieck, V. Greco, and S. Krishnaswamy: Exploring Epithelial-Cell Calcium Signaling With Geometric and Topological Data Analysis, ‘Geometrical and Topological Representation Learning’ Workshop at ICLR, 2021
[BibTeX] - J. Born†, N. Wiedemann†, M. Cossio, C. Buhre, G. Brändle, K. Leidermann, J. Goulet, A. Aujayeb, M. Moor, B. Rieck, and K. Borgwardt: Accelerating Detection of Lung Pathologies With Explainable Ultrasound Image Analysis, Applied Sciences, Volume 11, Number 2, 2021
[Preprint] • [GitHub] • [BibTeX] - A. C. Gumpinger, B. Rieck, D. G. Grimm, I. H. Consortium, and K. Borgwardt: Network-Guided Search for Genetic Heterogeneity Between Gene Pairs, Bioinformatics, Volume 37, Number 1, pp. 57–65, 2021
[GitHub] • [BibTeX]
2020
- B. Rieck, F. Sadlo, and H. Leitte: Persistence Concepts for 2D Skeleton Evolution Analysis, Topological Methods in Data Analysis and Visualization V, pp. 139–154, 2020
[Preprint] • [GitHub] • [BibTeX] - B. Rieck, F. Sadlo, and H. Leitte: Topological Machine Learning With Persistence Indicator Functions, Topological Methods in Data Analysis and Visualization V, pp. 87–101, 2020
[Preprint] • [BibTeX] - B. Rieck, M. Banagl, F. Sadlo, and H. Leitte: Persistent Intersection Homology for the Analysis of Discrete Data, Topological Methods in Data Analysis and Visualization V, pp. 37–51, 2020
[Preprint] • [BibTeX] - B. Rieck, F. Sadlo, and H. Leitte: Hierarchies and Ranks for Persistence Pairs, Topological Methods in Data Analysis and Visualization V, pp. 3–17, 2020
[Preprint] • [BibTeX] - S. Groha†, C. Weis†, A. Gusev, and B. Rieck: Topological Data Analysis of Copy Number Alterations in Cancer, ‘Learning Meaningful Representations of Life’ Workshop at NeurIPS, 2020
[Preprint] • [BibTeX] - S. C. Brüningk†, F. Hensel†, C. R. Jutzeler‡, and B. Rieck‡: Scalable Solutions for MR Image Classification of Alzheimer’s Disease, ‘Medical Imaging meets NeurIPS’ Workshop at NeurIPS, 2020
[BibTeX] - S. C. Brüningk†, F. Hensel†, C. R. Jutzeler‡, and B. Rieck‡: Image Analysis for Alzheimer’s Disease Prediction: Embracing Pathological Hallmarks for Model Architecture Design, ‘Machine Learning for Health (ML4H)’ Workshop at NeurIPS, 2020
[Preprint] • [GitHub] • [BibTeX] - K. Borgwardt, E. Ghisu, F. Llinares-López, L. O’Bray, and B. Rieck: Graph Kernels: State-of-the-Art and Future Challenges, Foundations and Trends® in Machine Learning, Volume 13, Number 5–6, pp. 531–712, 2020
[Preprint] • [GitHub] • [BibTeX] - M. Moor, M. Horn, K. Borgwardt, and B. Rieck: Challenging Euclidean Topological Autoencoders, ‘Topological Data Analysis and Beyond’ Workshop at NeurIPS, 2020
[GitHub] • [BibTeX] - B. Rieck†, T. Yates†, C. Bock, K. Borgwardt, G. Wolf, N. Turk-Browne‡, and S. Krishnaswamy‡: Uncovering the Topology of Time-Varying fMRI Data Using Cubical Persistence, Advances in Neural Information Processing Systems, Volume 33, pp. 6900–6912, 2020
[Preprint] • [GitHub] • [BibTeX]
Accepted as a spotlight presentation at NeurIPS (top 3% of all submissions) - B. Rieck†, T. Yates†, C. Bock, K. Borgwardt, G. Wolf, N. Turk-Browne‡, and S. Krishnaswamy‡: Topological Methods for fMRI Data, ICML Workshop on Computational Biology, 2020
[BibTeX] - C. Weis†, M. Horn†, B. Rieck†, A. Cuénod, A. Egli, and K. Borgwardt: Kernel-Based Antimicrobial Resistance Prediction From MALDI-TOF Mass Spectra, ICML Workshop on Machine Learning for Global Health, 2020
[BibTeX] - M. Moor, M. Horn, C. Bock, K. Borgwardt, and B. Rieck: Path Imputation Strategies for Signature Models, ICML Workshop on the Art of Learning with Missing Values (ARTEMISS), 2020
[BibTeX] - T. Gumbsch, C. Bock, M. Moor, B. Rieck, and K. Borgwardt: Enhancing Statistical Power in Temporal Biomarker Discovery Through Representative Shapelet Mining, Bioinformatics, Volume 36, Number Supplement_2, pp. i840–i848, 2020
[GitHub] • [BibTeX] - M. Moor†, M. Horn†, B. Rieck‡, and K. Borgwardt‡: Topological Autoencoders, Proceedings of the 37th International Conference on Machine Learning, Number 119, pp. 7045–7054, 2020
[Preprint] • [GitHub] • [BibTeX] - M. Horn, M. Moor, C. Bock, B. Rieck, and K. Borgwardt: Set Functions for Time Series, Proceedings of the 37th International Conference on Machine Learning, Number 119, pp. 4353–4363, 2020
[Preprint] • [GitHub] • [BibTeX] - C. D. Hofer, F. Graf, B. Rieck, M. Niethammer, and R. Kwitt: Graph Filtration Learning, Proceedings of the 37th International Conference on Machine Learning, Number 119, pp. 4314–4323, 2020
[Preprint] • [GitHub] • [BibTeX] - C. R. Jutzeler†, L. Bourguignon†, C. V. Weis, B. Tong, C. Wong, B. Rieck, H. Pargger, S. Tschudin-Sutter, A. Egli, K. Borgwardt‡, and M. Walter‡: Comorbidities, Clinical Signs and Symptoms, Laboratory Findings, Imaging Features, Treatment Strategies, and Outcomes in Adult and Pediatric Patients With COVID-19: A Systematic Review and Meta-Analysis, Travel Medicine and Infectious Disease, Volume 37, pp. 101825, 2020
[Preprint] • [BibTeX] - C. Weis†, M. Horn†, B. Rieck†, A. Cuénod, A. Egli, and K. Borgwardt: Topological and Kernel-Based Microbial Phenotype Prediction From MALDI-TOF Mass Spectra, Bioinformatics, Volume 36, Number Supplement_1, pp. i30–i38, 2020
[GitHub] • [BibTeX] - S. L. Hyland†, M. Faltys†, M. Hüser†, X. Lyu†, T. Gumbsch†, C. Esteban, C. Bock, M. Horn, M. Moor, B. Rieck, M. Zimmermann, D. Bodenham, K. Borgwardt‡, G. Rätsch‡, and T. M. Merz‡: Early Prediction of Circulatory Failure in the Intensive Care Unit Using Machine Learning, Nature Medicine, Volume 26, Number 3, pp. 364–373, 2020
[GitHub] • [BibTeX]
2019
- M. Togninalli†, E. Ghisu†, F. Llinares-López, B. Rieck, and K. Borgwardt: Wasserstein Weisfeiler–Lehman Graph Kernels, Advances in Neural Information Processing Systems, Volume 32, pp. 6436–6446, 2019
[Preprint] • [GitHub] • [BibTeX]
Accepted as a spotlight presentation at NeurIPS (top 3% of all submissions) - C. Bock†, M. Togninalli†, E. Ghisu, T. Gumbsch, B. Rieck, and K. Borgwardt: A Wasserstein Subsequence Kernel for Time Series, ‘Optimal Transport & Machine Learning’ Workshop at NeurIPS, 2019
[GitHub] • [BibTeX] - C. Bock†, M. Togninalli†, E. Ghisu, T. Gumbsch, B. Rieck, and K. Borgwardt: A Wasserstein Subsequence Kernel for Time Series, Proceedings of the 19th IEEE International Conference on Data Mining (ICDM), pp. 964–969, 2019
[Author’s copy] • [GitHub] • [BibTeX] - M. Moor, M. Horn, B. Rieck, D. Roqueiro, and K. Borgwardt: Early Recognition of Sepsis With Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping, Proceedings of the 4th Machine Learning for Healthcare Conference, Number 106, pp. 2–26, 2019
[Preprint] • [GitHub] • [BibTeX] - B. Rieck†, C. Bock†, and K. Borgwardt: A Persistent Weisfeiler–Lehman Procedure for Graph Classification, Proceedings of the 36th International Conference on Machine Learning, Number 97, pp. 5448–5458, 2019
[GitHub] • [BibTeX] - B. Zheng, B. Rieck, H. Leitte, and F. Sadlo: Visualization of Equivalence in 2D Bivariate Fields, Computer Graphics Forum, Volume 38, Number 3, pp. 311–323, 2019
[Author’s copy] • [BibTeX] - B. Rieck†, M. Togninalli†, C. Bock†, M. Moor, M. Horn, T. Gumbsch, and K. Borgwardt: Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology, International Conference on Learning Representations, 2019
[Preprint] • [GitHub] • [BibTeX]
2018
- C. Bock, T. Gumbsch, M. Moor, B. Rieck, D. Roqueiro, and K. Borgwardt: Association Mapping in Biomedical Time Series via Statistically Significant Shapelet Mining, Bioinformatics, Volume 34, Number 13, pp. i438–i446, 2018
[GitHub] • [BibTeX] - K. Sdeo, B. Rieck, and F. Sadlo: Visualization of Fullerene Fragmentation, Proceedings of IEEE Pacific Visualization Symposium (PacificVis), pp. 111–115, 2018
[Author’s copy] • [BibTeX] - L. Hofmann, B. Rieck, and F. Sadlo: Visualization of 4D Vector Field Topology, Computer Graphics Forum, Volume 37, Number 3, pp. 301–313, 2018
[Author’s copy] • [BibTeX] - K. Hanser, O. Klein, B. Rieck, B. Wiebe, T. Selz, M. Piatkowski, A. Sagristà, B. Zheng, M. Lukácová-Medvidová, G. Craig, H. Leitte, and F. Sadlo: Visualization of Parameter Sensitivity of 2D Time-Dependent Flow, Advances in Visual Computing (Proceedings of the 13th International Symposium on Visual Computing), pp. 359–370, 2018
[BibTeX] - B. Rieck, U. Fugacci, J. Lukasczyk, and H. Leitte: Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks, IEEE Transactions on Visualization and Computer Graphics, Volume 24, Number 1, pp. 822–831, 2018
[Author’s copy] • [GitHub] • [BibTeX]
2017
- B. Rieck: Persistent Homology in Multivariate Data Visualization, Ph.D. thesis, Heidelberg University, 2017
[Author’s copy] • [BibTeX] - B. Rieck, H. Leitte, and F. Sadlo: Persistence Concepts for 2D Skeleton Evolution Analysis, Workshop on Topology-Based Methods in Visualization (TopoInVis), 2017
[GitHub] • [BibTeX] - B. Rieck, H. Leitte, and F. Sadlo: Hierarchies and Ranks for Persistence Pairs, Workshop on Topology-Based Methods in Visualization (TopoInVis), 2017
[BibTeX]
Award for the best extended abstract - B. Rieck and H. Leitte: Agreement Analysis of Quality Measures for Dimensionality Reduction, Topological Methods in Data Analysis and Visualization IV, pp. 103–117, 2017
[Author’s copy] • [BibTeX]
2016
- B. Rieck and H. Leitte: ‘Shall I Compare Thee to a Network?’ — Visualizing the Topological Structure of Shakespeare’s Plays, Workshop on Visualization for the Digital Humanities at IEEE Vis, 2016
[GitHub] • [BibTeX] - B. Rieck and H. Leitte: Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology, Computer Graphics Forum, Volume 35, Number 3, pp. 81–90, 2016
[Author’s copy] • [BibTeX] - J. Fangerau, B. Höckendorf, B. Rieck, C. Heine, J. Wittbrodt, and H. Leitte: Interactive Similarity Analysis and Error Detection in Large Tree Collections, Visualization in Medicine and Life Sciences III, pp. 287–307, 2016
[BibTeX]
2015
- B. Rieck and H. Leitte: Comparing Dimensionality Reduction Methods Using Data Descriptor Landscapes, Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2015
[BibTeX] - B. Rieck and H. Leitte: Persistent Homology for the Evaluation of Dimensionality Reduction Schemes, Computer Graphics Forum, Volume 34, Number 3, pp. 431–440, 2015
[Author’s copy] • [BibTeX] - B. Rieck and H. Leitte: Agreement Analysis of Quality Measures for Dimensionality Reduction, Workshop on Topology-Based Methods in Visualization (TopoInVis), 2015
[BibTeX]
2014
- B. Rieck and H. Leitte: Enhancing Comparative Model Analysis Using Persistent Homology, IEEE Vis Workshop on Visualization for Predictive Analytics, 2014
[BibTeX] - B. Rieck and H. Leitte: Structural Analysis of Multivariate Point Clouds Using Simplicial Chains, Computer Graphics Forum, Volume 33, Number 8, pp. 28–37, 2014
[Author’s copy] • [BibTeX]
2013
- B. Rieck, H. Mara, and S. Krömker: Unwrapping Highly-Detailed 3D Meshes of Rotationally Symmetric Man-Made Objects, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W1, pp. 259–264, 2013
[BibTeX] - M. Forbriger, H. Mara, B. Rieck, C. Siart, and O. Wagener: Der “Gesprengte Turm” Am Heidelberger Schloss – Untersuchung Eines Kulturdenkmals Mithilfe Hoch Auflösender Terrestrischer Laserscans, Denkmalpflege in Baden-Württemberg, Nachrichtenblatt der Landesdenkmalpflege, Volume 3, pp. 165–168, 2013
[BibTeX]
2012
- B. Rieck, H. Mara, and H. Leitte: Multivariate Data Analysis Using Persistence-Based Filtering and Topological Signatures, IEEE Transactions on Visualization and Computer Graphics, Volume 18, Number 12, pp. 2382–2391, 2012
[Author’s copy] • [BibTeX]
2011
- B. Rieck: Smoothness Analysis of Subdivision Algorithms, M.Sc. thesis, Heidelberg University, 2011
[Author’s copy] • [GitHub] • [BibTeX]
Notes
Here are some notes on various topics. These notes are not peer-reviewed, though, and may contain errors—please notify me if you find some.
Research interests
Our world is full of complex phenomena that happen at different temporal or spatial scales. I am interested in developing methods and techniques that help us understand these phenomena, especially in the presence of noisy or time-varying measurements.
Broadly speaking, this means that I have a penchant for developing new topological machine learning techniques, i.e. algorithms that are ‘aware’ of the fundamental topological properties of a data set. If you are familiar with machine learning terminology, this translates to developing new inductive biases and new architectures.
(I also have a general interest in algebraic & differential topology—both fields contain a wealth of potentially useful tools for visualisation and machine learning!)
Since ’theory without practice is empty,’ I also aim to build bridges to applications, in particular those that arise in the life sciences or healthcare in general. These domains are a source of complex data sets and challenges, and solutions to these challenges promise to have a profound, positive impact on society. As a mathematician-turned-computer-scientist-turned-something-else, this sparks immense joy and gratitude!
I am fortunate to work with many outstanding human beings, some of which also have personal websites: