Michael Möller

Michael Möller is a Professor for Computer Vision at the University of Siegen, Germany. His research focuses on integrating model-based and learning-based techniques in imaging and vision, with an emphasis on efficient optimization algorithms for solving high-dimensional minimization problems.

Research Interests:

• Combining classical model-based methods with deep learning
• Optimization algorithms for inverse problems
• Applications in CT reconstruction, image segmentation, and quantum computing
• Development of energy-dissipating neural networks and non-smooth regularization techniques

Scientific Awards:

• Best Paper Award at ISWC 2021
• Best Paper Honorable Mention at GCPR 2020
• Best Paper Award at VMV 2015

Collaborations and Grants: Active collaborations with institutions like IEEE, Springer, and universities across Europe. Publications in top-tier venues such as NeurIPS, CVPR, ICCV, and ICLR. His work spans theoretical frameworks (e.g., convex relaxations, spectral methods) and practical applications (e.g., medical imaging, THz defect detection).