| Current Publications | • Peivandi, Armin Darius; Holtkamp, Michael; Rave, Hennes; Linsen, Lars; Martens, Sven; Müller, Klaus-Michael; Karst, Uwe; Martens, Sabrina Rapid versus slow degeneration and complications of biomaterials in patients with congenital heart disease. Cardiovascular Pathology Vol. 75, 2025 online • Molchanov, Vladimir; Rave, Hennes; Linsen, Lars Efficient Regularization-based Normalization for Interactive Multidimensional Data Analysis Without Scaling Artifacts. Journal of WSCG Vol. 33 (1), 2025 online • Kronenberg, Katharina; Rave, Hennes; Ghaffari-Tabrizi-Wizsy, Nassim; Nyckees, Danae; Elinkmann, Matthias; Freitak, Dalial; Linsen, Lars; Gonzalez de Vega, Raquel; Clases, David Exploring high-dimensional LA-ICP-TOFMS data with uniform manifold approximation and projection (UMAP). Journal of Analytical Atomic Spectrometry Vol. 0, 2025 online • Molchanov, Vladimir*; Rave, Hennes*; Linsen, Lars A Decluttering Lens for Scatterplots. , 2025 online • Cutura, Rene; Rave, Hennes; Ngo, Quynh Quang; Molchanov, Vladimir; Linsen, Lars; Weiskopf, Daniel; Sedlmair, Michael SiGrid: Gridifying Scatterplots with Sector-Based Regularization and Hagrid. , 2025 online • Rave, Hennes; Molchanov, Vladimir; Linsen, Lars De-cluttering Scatterplots with Integral Images. IEEE Transactions on Visualization and Computer Graphics Vol. 0 (0), 2024 online • Rave, Hennes; Evers, Marina; Gerrits, Tim; Linsen, Lars Region-based Visualization in Hierarchically Clustered Ensemble Volumes. , 2024 online • Evers, Marina; Leistikow, Simon; Rave, Hennes; Linsen, Lars Interactive Visual Analysis of Spatial Sensitivities. IEEE Transactions on Visualization and Computer Graphics Vol. 0 (0), 2024 online • Rave, Hennes; Molchanov, Vladimir; Linsen, Lars Uniform Sample Distribution in Scatterplots via Sector-based Transformation. , 2024 online |
| Current Projects | • CRC 1450 - Z01: Interactive and computational analysis of large multiscale imaging data The multiscale imaging strategy central to this initiative imposes novel data analysis challenges. The high complexity of the acquired data results from their nature of being volumetric, time-varying, large, multiscale, and forming cohorts). Meeting these challenges requires basic research in the fields of image analysis, machine learning, and visualization. Machine learning will be used to uncover inherent relationships between patterns at multiple scales. An interactive visual approach supports the user-centric analysis of detected features. The deliverable of this project will be generally applicable, effective, and efficient methods supporting the overall goal of multiscale data analysis. | hennes.rave@uni-muenster.de |
| Phone | +49 251 83-33751 |
| Room | 605 |
| Secretary | Sekretariat Sichma Frau Katharina Sichma Telefon +49 251 83-32700 Zimmer 604b |
| Address | Herr Hennes Rave Institut für Informatik Fachbereich Mathematik und Informatik der Universität Münster Einsteinstrasse 62 48149 Münster Deutschland |
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