Publications

  1. Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference: Simon Welker et al., arXiv, doi: 10.48550/arXiv.2509.25269
  2. Hypergraph p-Laplacian Equations for Data Interpolation and Semi-supervised Learning: Kehan Shi et al., J Sci Comput, doi: 10.1007/s10915-025-02908-y
  3. Global $q$-dependent inverse transforms of intensity autocorrelation data: Tobias Eklund et al., arXiv, doi: 10.48550/arXiv.2507.14106
  4. AlphaPulldown2—a general pipeline for high-throughput structural modeling: Dmitry Molodenskiy et al., null, doi: 10.1093/bioinformatics/btaf115
  5. Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram: Xiaogang Yang et al., Opt. Express, doi: 10.1364/OE.569216
  6. Analysis of mean-field models arising from self-attention dynamics in transformer architectures with layer normalization: Martin Burger et al., arXiv, doi: 10.48550/arXiv.2501.03096
  7. Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs: Álvaro Fernández Corral et al., arXiv, doi: 10.48550/arXiv.2409.16826
  8. Machine learning for the reconstruction and analysis of synchrotron-radiation tomography data: Julian P. Moosmann et al., null, doi: 10.1117/12.3028018
  9. Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge: Valentin Munteanu et al., J Appl Cryst, doi: 10.1107/S1600576724002115
  10. PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms: Erik Genthe et al. et al., Journal of Structural Biology, doi: 10.1016/j.jsb.2023.107990
  11. Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data: Vladimir Starostin et al., npj Comput Mater, doi: 10.1038/s41524-022-00778-8
  12. The osteogenetic activities of mesenchymal stem cells in response to Mg2+ ions and inflammatory cytokines: a numerical approach using fuzzy logic controllers: Jalil Nourisa et al., PLoS Comput Biol, doi: 10.1371/journal.pcbi.1010482
  13. Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering: Alessandro Greco et al., J Appl Cryst, doi: 10.1107/S1600576722002230
  14. An active learning approach for the interactive and guided segmentation of tomography data: Bashir Kazimi et al., SPIE Optical Engineering + Applications, doi: 10.1117/12.2637973
  15. Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering: Alessandro Greco et al., arXiv:2202.11609 [physics.data-an], doi: 10.48550/arXiv.2202.11609
  16. End-to-End Deep Learning Pipeline for Real-Time Processing of Surface Scattering Data at Synchrotron Facilities: Vladimir Starostin et al., Synchrotron Radiation News, doi: 10.1080/08940886.2022.2112499
  17. PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets: Yuhe Zhang et al., Opt. Express, doi: 10.1364/OE.423222
  18. Machine learning denoising of high-resolution X-ray nanotomography data: Silja Flenner et al., J Synchrotron Radiat, doi: 10.1107/S1600577521011139
  19. X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease: Sebastian Günther et al., Science, doi: 10.1126/science.abf7945
  20. High-resolution ex vivo analysis of the degradation and osseointegration of Mg-xGd implant screws in 3D: Diana Krüger et al., Bioactive Materials, doi: 10.1016/j.bioactmat.2021.10.041
  21. Scaling the U-net: segmentation of biodegradable bone implants in high-resolution synchrotron radiation microtomograms: Ivo M. Baltruschat et al., Sci Rep, doi: 10.1038/s41598-021-03542-y
  22. Suppression of thermal nanoplasma emission in clusters strongly ionized by hard x-rays: Yoshiaki Kumagai et al., J. Phys. B: At. Mol. Opt. Phys., doi: 10.1088/1361-6455/abd878
  23. Few-fs resolution of a photoactive protein traversing a conical intersection: A. Hosseinizadeh et al., Nature, doi: 10.1038/s41586-021-04050-9
  24. Tomographic reconstruction with a generative adversarial network: Xiaogang Yang et al., J Synchrotron Radiat, doi: 10.1107/S1600577520000831
  25. Integration of machine learning with phase field method to model the electromigration induced Cu6Sn5 IMC growth at anode side Cu/Sn interface: Anil Kunwar et al., Journal of Materials Science & Technology, doi: 10.1016/j.jmst.2020.04.046
  26. Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments: Silja Flenner et al., J Synchrotron Radiat, doi: 10.1107/S1600577520007407
  27. Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function: Tobias Strohmann et al., Sci Rep, doi: 10.1038/s41598-019-56008-7
  28. A load frame for in situ tomography at PETRA III: Julian Moosmann et al., Proceedings Volume 11113, Developments in X-Ray Tomography XII, doi: 10.1117/12.2530445
  29. Silver clusters shape determination from in-situ XANES data: Janis Timoshenko et al., Radiation Physics and Chemistry, doi: 10.1016/j.radphyschem.2018.11.003
  30. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning: A. Sanchez-Gonzalez et al., Nat Commun, doi: 10.1038/ncomms15461