معرفی
Kristian Bredies is a Professor of Applied Mathematics at the Department of Mathematics and Scientific Computing, University of Graz, Austria, where he leads the research group "Inverse Problems and Mathematical Imaging". His work focuses on developing mathematical methods for solving inverse problems in medical imaging applications including MRI and CT reconstruction.
His research spans mathematical image processing, inverse problems, data-driven methods, and optimization algorithms. Key areas include variational methods, multi-order regularization, sparsity constraints, dynamic optimal-transport approaches, operator learning, and preconditioning techniques for first-order optimization. His group bridges theoretical analysis with practical medical imaging applications to overcome physical limitations in current diagnostic technologies.
Recent publications demonstrate a concentrated focus on optimal transport theory applied to dynamic inverse problems, featuring novel conditional gradient methods and mathematical frameworks for motion-aware imaging. These works advance reconstruction techniques for medical imaging by integrating mathematical rigor with computational efficiency.
No scientific awards were documented in the source material.
Professor Bredies directs major research initiatives including:
- MR-DYNAMO: Austrian Science Fund (FWF) Special Research Area (SFB) F 100800 (2025-2029)
- Next Generation CEST MRI: FWF/DFG Project I 4870 (2020-2025)
- TraDE-Opt: EU H2020 Marie Skłodowska-Curie ITN (2020-2024)
- PIR 27: FWF/CDG Project on motion-aware medical imaging (2017-2020)
These projects involve international interdisciplinary collaboration and have trained early-career researchers in mathematical optimization for medical imaging.
He heads the "Inverse Problems and Mathematical Imaging" research group at the University of Graz, which maintains strong international partnerships including the International Research Training Group IGDK Munich-Graz. The group's work integrates theoretical development, algorithm design, and practical implementation to address critical challenges in medical imaging reconstruction.
