معرفی
Anders Heyden is a Professor at the Centre for Mathematical Sciences, Lund University, serving as deputy dean at the Faculty of Engineering with responsibility for research and research education. He is affiliated with multiple research initiatives including the Mathematical Imaging Group, eSSENCE: The e-Science Collaboration, and ELLIIT: the Linköping-Lund initiative on IT and mobile communication. His profile areas include Engineering Health, AI and Digitalization, Natural and Artificial Cognition, and Proactive Ageing at Lund University.
Heyden received his M.Sc. in Engineering Physics in 1989 and Ph.D. in Mathematics in 1995 from Lund University. His doctoral thesis, "Geometry and algebra of multiple projective transformations," focused on computer vision. He has progressed through academic ranks from post-doctoral research fellow to associate professor and is now a full professor.
His research spans image analysis, pattern recognition, machine learning, computer vision for autonomous systems, and medical image analysis. His work contributes to multiple UN Sustainable Development Goals, particularly in medical imaging, geometry, and computer graphics. His research fingerprint shows strong activity in images engineering (100%), mathematics (43%), algorithms (36%), calibration (29%), models (24%), projective geometry (18%), experiments (18%), and homography (18%).
- IAPR fellow
- Sparbanken Skåne Innovation Award 2023
- Senior member of IEEE
- Member of the Royal Swedish Physiographic Society
- Former president of the Swedish Society for Automated Image Analysis (2010-2014)
- Former member of the International Association for Pattern Recognition governing board (2010-2018)
Heyden has co-founded three startup companies: Ludesi, Mometric, and 23°N, with the latter winning the Sparbanken Skåne Innovation Award 2023. His supervised work includes 28 students and researchers across various projects. Current projects include EDAP (Early diagnostics and prognostics of Alzheimer's disease), machine learning-based image analysis of antibody function, Semantic Structure from Motion, DOGS (Digital Pathology for Optimized Gleason Score), and ELLIIT LU P07: Deep Vision: Multiple Object Tracking.
