معرفی
Stefano Cagnoni is an Associate Professor in the Department of Computer Engineering at the University of Parma, Italy. He holds a PhD in Bioengineering from the University of Florence and has been a key academic figure since joining the University of Parma in 1997. His research spans soft computing, evolutionary computation, neural networks, and their applications in computer vision and biomedical imaging.
- PhD in Bioengineering, University of Florence (1993)
- Bachelor’s in Electronic Engineering, University of Florence (1988)
His primary research interests include evolutionary computation, neural networks, pattern recognition, and computer vision. He has pioneered work in genetic programming, particle swarm optimization, and GPU-accelerated computing for complex system analysis. His applied research focuses on biomedical image segmentation, autonomous robotics, and signal processing, with a strong emphasis on real-world implementation and industrial collaboration.
The recent publications highlight a shift toward interdisciplinary applications, including machine learning for food authenticity, financial document analysis, and healthcare monitoring systems. These works reflect a consistent use of advanced computational intelligence techniques across diverse domains such as food science, finance, and medical informatics.
Scientific Awards:
- Evostar 2009 Award for outstanding contributions to Evolutionary Computation
Stefano Cagnoni has supervised numerous research projects funded by MIUR, CNR, ASI, ENEA, and the EU (including the Marie Skłodowska-Curie MIBISOC project). He has also led industrial collaborations, such as a computer vision-based train pantograph inspection system that led to a patented industrial product. He has organized major international events like EvoApplications and WIVACE and has held editorial roles in journals such as the Journal of Artificial Evolution and Applications.
He leads research in evolutionary computation and soft computing, having founded GSICE (now WIVACE) and co-chaired MedGEC. His lab focuses on developing intelligent systems for image analysis, signal processing, and autonomous navigation, often using bio-inspired algorithms and parallel computing architectures.
