Scholar
Lea Bogensperger
Google Scholar ID: 4cNGQ0sAAAAJ
University of Zurich
medical imaging
optimization
machine learning
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Citations & Impact
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Citations
63
H-index
4
i10-index
3
Publications
16
Co-authors
16
list available
Contact
Email
lea.bogensperger@uzh.ch
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Publications
6 items
Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing
2026
Cited
0
Sparse Concept Channels in Frozen 3D CT Vision Encoders
2026
Cited
0
Generating Physically Consistent Molecules with Energy-Based Models
2026
Cited
0
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
2025
Cited
0
Understanding, Accelerating, and Improving MeanFlow Training
2025
Cited
0
FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms
arXiv.org · 2024
Cited
1
Resume
Academic Achievements
Published "Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling" at NeurIPS 2025.
Published "Flow Matching-Based Data Synthesis for Robust Anatomical Landmark Localization" in IEEE Journal of Biomedical and Health Informatics, 2025.
Co-developed CRISPR-PAMdb database and CICERO protein language model; manuscript under review (2025), preprint on bioRxiv.
Published "A Variational Perspective on Generative Protein Fitness Optimization" at ICML 2025.
Published "FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transforms" in International Journal of Computer Vision, 2025.
Published "An Adaptively Inexact Method for Bilevel Learning Using Primal-Dual Style Differentiation" in Journal of Mathematical Imaging and Vision.
Co-authors
12 total
Thomas Pock
Professor of Computer Science, TU Graz
Dominik Narnhofer
Postdoc ETH Zurich
Antonin Chambolle
CEREMADE, CNRS & Université Paris-Dauphine, PSL Research University
Konrad Schindler
Professor of Photogrammetry and Remote Sensing, ETH Zurich
Martin Urschler
Associate Professor Medical Image Analysis at Medical University Graz
Arnela Hadzic
Medical University of Graz
Alexander Falk
Graz University of Technology
Erich Kobler
Institute for Machine Learning, Johannes Kepler University Linz