Welcome

Welcome to the website of the AIDOS LAB at the Institute of AI for Health, an institute of the Helmholtz Zentrum München! We are fascinated by discovering hidden structures in complex data sets, in particular those arising in healthcare applications.

Our primary research interests are situated at the intersection of geometrical deep learning, topological machine learning, and representation learning. We want to make use of geometrical and topological information—also known as manifold learning—to imbue neural networks with more information in their respective tasks, leading to better and more robust outcomes.

Following the dictum ’theory without practice is empty,’ we also develop methods to address challenges in biomedicine or healthcare applications. Of particular interest are the analysis of MRI data sets to improve our understanding of human cognition and neurodegenerative disorders, as well as the analysis of multivariate clinical time series to detect and prevent the onset of sepsis or myocardial ischemia.

About

‘AIDOS’ has two meanings that complement each other well. The first meaning refers to our mission statement, viz. to develop Artificial Intelligence for Discovering Obscured Shapes. The second meaning originates from the Greek word ‘αἰδώς,’ which means ‘awe,’ ‘reverence,’ or ‘humility.’ This awe or humility should serve as one of our guiding principles when we work on challenging problems in healthcare research, aiming to improve our world using machine learning.

Members & Collaborators

Your name is missing here! Learn more about joining us below.

Alumni & Alumnae

• Marek Cerny (now a Ph.D. student with Floris Geerts at the University of Antwerp)
• Ferdinand Hölzl (now a master’s student at University of Hamburg)

Publications

Here are all publications of lab members, sorted by year. Publications appear in the order in which they are accepted.

Preprints

• K. V. Nadimpalli, A. Chattopadhyay^‡, and B. Rieck^‡: Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images From Single 2D Slices, Preprint, 2023
  [Preprint] • [BibTeX]
• B. Rieck: On the Expressivity of Persistent Homology in Graph Learning, Preprint, 2023
  [Preprint] • [BibTeX]
• J. Southern^†, J. Wayland^†, M. Bronstein, and B. Rieck: Curvature Filtrations for Graph Generative Model Evaluation, Preprint, 2023
  [Preprint] • [BibTeX]
• J. von Rohrscheidt and B. Rieck: TOAST: Topological Algorithm for Singularity Tracking, Preprint, 2022
  [Preprint] • [GitHub] • [BibTeX]
• C. Coupette, J. Vreeken, and B. Rieck: All the World’s a (Hyper)Graph: A Data Drama, Preprint, 2022
  [Preprint] • [GitHub] • [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]
• 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, Preprint, 2021
  [Preprint] • [BibTeX]
• M. F. Adamer, L. O’Bray, E. De Brouwer, B. Rieck^‡, and K. Borgwardt^‡: The Magnitude Vector of Images, Preprint, 2021
  [Preprint] • [BibTeX]
• J. L. Moore^†, F. Gao^†, C. Matte-Martone, S. Du, E. Lathrop, S. Ganesan, L. Shao, D. Bhaskar, A. Cox, C. Hendry, B. Rieck, S. Krishnaswamy^‡, and V. Greco^‡: Tissue-Wide Coordination of Calcium Signaling Regulates the Epithelial Stem Cell Pool During Homeostasis, Preprint, 2021
  [Preprint] • [BibTeX]
• M. Moor^†, N. Bennet^†, D. Plecko^†, M. Horn^†, B. Rieck, N. Meinshausen, P. Bühlmann, and K. Borgwardt: Predicting Sepsis in Multi-Site, Multi-National Intensive Care Cohorts Using Deep Learning, Preprint, 2021
  [Preprint] • [BibTeX]
• B. Rieck: Basic Analysis of Bin-Packing Heuristics, Preprint, 2021
  [Preprint] • [GitHub] • [BibTeX]
• M. Kuchroo^†, M. DiStasio^†, E. Calapkulu, M. Ige, L. Zhang, A. H. Sheth, M. Menon, Y. Xing, S. Gigante, J. Huang, R. M. Dhodapkar, B. Rieck, G. Wolf^‡, S. Krishnaswamy^‡, and B. P. Hafler: Topological Analysis of Single-Cell Data Reveals Shared Glial Landscape of Macular Degeneration and Neurodegenerative Diseases, Preprint, 2021
  [Preprint] • [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)

2023

• C. Coupette, S. Dalleiger, and B. Rieck: Ollivier–Ricci Curvature for Hypergraphs: A Unified Framework, International Conference on Learning Representations (ICLR), 2023 (in press)
  [Preprint] • [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 (in press)
  [Preprint] • [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, 2023 (in press)
  [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 (NeurIPS), 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]
• 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 System (NeurIPS), 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 (ICLR), 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 (ICLR), 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 (KDD), 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 (NeurIPS), 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 (ICML), 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 (ICML), 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 (ICML), 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 (NeurIPS), 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 (ICML), 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 (ICLR), 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]

Join us

Thanks for your interest in our group! Why not consider joining the team? We are seeking students (at all levels) with strong quantitative backgrounds (computer science, mathematics, physics, …). You should be interested in working at the intersection of different fields and feel comfortable about writing code.

Since the group is still starting to establish itself, you have the unique opportunity to truly shape and influence things here.

We are not interested in ’leader-board science’ or ‘chasing the state-of-the-art’ in a table. That is not to say that we are not interested in producing relevant methods! Our overarching goal is to produce excellent science using methods whose performance we can explain and understand. This necessitates comprehensive comparisons with other methods, ablation studies, and many additional tricks to figure out what is going on. If this sounds enticing to you, we would love to hear from you!

To learn more about our working style, see this note for potential student collaborators.

Bachelor’s and master’s theses

If you are interested in writing your thesis with us, please send your CV, your transcript of records, and a brief cover letter stating your research interests to bastian.rieck@helmholtz-muenchen.de. Your cover letter should provide answers to the following questions:

1. Are there any research papers of the group that pique your interest specifically? (You do not have to be an expert in the topic yet, of course!)

2. Are you more comfortable working empirically, i.e. lots of implementations, experiments, and so on, or working theoretically, i.e. lots of proofs, derivations, and more?

3. When would you be available for working on the thesis?

Ph.D. positions

If you are interested in working with us and have a background in mathematics, computer science, physics, or a general penchant for computational methods, please send your CV to bastian.rieck@helmholtz-muenchen.de for general inquiries on Ph.D. positions.

Research visits

If you are interested in a short-term research opportunity, such as a research visit over the summer, please reach out bastian.rieck@helmholtz-muenchen.de. We are particularly interested in stays that may result in long-term collaborations.

If you are looking for an internship, note that our institution does not permit paid internships. There are funding opportunities for such internships available; check out RISE or DAAD Scholarships in general.

How to find us

We are located at the Neuherberg Campus of Helmholtz Munich. Here is a map of the campus (click for larger version):