Microscopic Image Analysis

Research projects

Here are some of the projects members of this group work and have worked on.

Current projects

• none

Completed projects

• none

Publications

• A dimension-independent bound on the Wasserstein contraction rate of a geodesic random walk on the sphere
  Philip Schär, Thilo Stier
  Electronic Communications in Probability 29, article 62, 2024
  Link to Paper

• Scaling up unbiased search-based symbolic regression
  Paul Kahlmeyer, Joachim Giesen, Michael Habeck, Henrik Voigt
  Proceedings of the 33rd International Joint Conference on Artificial Intelligence, pp. 4264-4272, 2024
  Link to Paper

• Parallel affine transformation tuning of Markov chain Monte Carlo
  Philip Schär, Michael Habeck, Daniel Rudolf
  Proceedings of the 41st International Conference on Machine Learning (ICML).
  PMLR 235, pp. 43571-43607, 2024
  Link to Paper
  Link to Source Code

• Bayesian multi-exposure image fusion for robust high dynamic range ptychography
  Shantanu Kodgirwar, Lars Loetgering, Chang Liu, Aleena Joseph, Leona Licht, Daniel S. Penagos Molina, Wilhelm Eschen, Jan Rothhardt, Michael Habeck
  Optics Express 32 (16), pp. 28090-28099, 2024
  Link to Paper
  Link to Source Code
  Link to Data

• 3D Computational Modeling of Defective Early Endosome Distribution in Human iPSC-Based Cardiomyopathy Models
  Hafiza Nosheen Saleem, Nadezda Ignatyeva, Christiaan Stuut, Stefan Jakobs, Michael Habeck, Antje Ebert
  Cells 13(11), article 923, 2024
  Link to Paper

• Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy
  Ruheen Wali, Hang Xu, Cleophas Cheruiyot, Hafiza Nosheen Saleem, Andreas Janshoff, Michael Habeck, Antje Ebert
  Biological Chemistry 405(6), pp. 427-439, 2024
  Link to Paper

• Matching biomolecular structures by registration of point clouds
  Felix Lambrecht, Andreas Kröpelin, Mario Lüttich, Michael Habeck, David Haselbach, Holger Stark
  arXiv preprint arXiv:2402.11589
  Link to Paper

• Matching biomolecular structures by registration of point clouds
  Michael Habeck, Andreas Kröpelin, Nima Vakili
  arXiv preprint arXiv:2401.12082
  Link to Paper

• Wasserstein contraction and spectral gap of slice sampling revisited
  Philip Schär
  Electronic Journal of Probability 28, article 136, 2023
  Link to Paper

• Dimension-independent spectral gap of polar slice sampling
  Daniel Rudolf, Philip Schär
  Statistics and Computing 34(1), article 20, 2024
  Link to Paper

• Bayesian maximum entropy ensemble refinement
  Benjamin Eltzner, Julian Hofstadler, Daniel Rudolf, Michael Habeck, Bert de Groot
  bioRxiv preprint bioRxiv:2023.09.12.557310, 2023
  Link to Paper

• Bayesian methods in integrative structure modeling
  Michael Habeck
  Biological Chemistry 404(8-9), pp. 741-754, 2023
  Link to Paper

• Gibbsian polar slice sampling
  Philip Schär, Michael Habeck, Daniel Rudolf
  Proceedings of the 40th International Conference on Machine Learning (ICML),
  PMLR 202, pp. 30204-30223, 2023
  Link to Paper
  Link to Source Code

• Geodesic slice sampling on the sphere
  Michael Habeck, Mareike Hasenpflug, Shantanu Kodgirwar, Daniel Rudolf
  arXiv preprint arXiv:2301.08056, 2023
  Link to Paper
  Link to Source Code

• Nested sampling for physical scientists
  Greg Ashton, Noam Bernstein, Johannes Buchner, Xi Chen, Gábor Csányi, Andrew Fowlie, Farhan Feroz, Matthew Griffiths, Will Handley, Michael Habeck, Edward Higson, Michael Hobson, Anthony Lasenby, David Parkinson, Livia B. Pártay, Matthew Pitkin, Doris Schneider, Joshua S. Speagle, Leah South, John Veitch, Philipp Wacker, David J. Wales, David Yallup
  Nature Reviews Methods Primers 2, article 39, 2022
  Link to Paper