معرفی
Axel Flinth is an Assistant Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, focusing on mathematical foundations of machine learning for pattern recognition in large datasets.
Education:
- PhD in Mathematics from Technische Universität Berlin (2018)
His research centers on compressed sensing—reconstructing signals from incomplete data using structural assumptions—and equivariance in deep neural networks, investigating how data symmetries can be leveraged to enhance model performance. He actively contributes to geometric deep learning and statistical inference for spatio-temporal data through membership in specialized research groups.
Recent publications (2022-2025) reveal a cohesive trajectory in mathematical optimization and symmetry-aware deep learning, with applications spanning computer vision (e.g., rotation-equivariant architectures for point clouds), wireless communication security, and signal reconstruction. His work consistently bridges theoretical mathematics with practical machine learning implementations.
Scientific Awards:
- No awards documented in available sources
Grants and Projects:
- Lead Researcher: Trade-offs in Nonconvex Learning (April 2022 - March 2027)
Research Affiliations:
- Geometric Deep Learning Group
- Statistical Learning and Inference for Spatio-Temporal Data
