Concetto SPAMPINATO
Keywords
- Computer Vision
- Machine Learning and Deep Learning
- Continual Learning
- Generative and Foundation Models
- Multimodal and Vision-Language Learning
- AI for Biomedical and Underwater Applications
Updated on 23 September 2026
Concetto Spampinato received his five-year degree in Computer Engineering (110/110 cum laude) from the University of Catania in 2004. From 2005 to 2008, he pursued his Ph.D. at the same university, receiving a Ph.D. in Computer Engineering and Telecommunications. During his training and subsequent academic career, he spent research periods at prestigious international institutions, including the School of Informatics at the University of Edinburgh (UK), Queen Mary University of London (UK), and the University of Central Florida (USA).
From 2009 to 2014, he was a Research Fellow at the University of Catania and, from December 2014, a Researcher at the Department of Electrical, Electronic and Computer Engineering (DIEEI), in the field of Information Processing Systems. Since 1 November 2021, he has been a Full Professor of Computer Engineering at the University of Catania. Since 2018, he has also been a Courtesy Faculty Member at the Center for Research in Computer Vision, University of Central Florida (USA), and he is also a Courtesy Faculty Member at Northwestern University (USA).
He is the founder and head of the PeRCeiVe.AI Lab (Pattern Recognition and Computer Vision Laboratory) at the University of Catania, which currently comprises approximately 25 members, including faculty members, researchers, postdoctoral researchers, Ph.D. students, and technical staff.
He has served as Area Chair for several of the leading international conferences in Artificial Intelligence, Machine Learning, and Computer Vision, including NeurIPS, CVPR, ECCV, AAAI, and WACV, and regularly serves on the program committees of major international conferences in these fields. He has also held editorial roles for international journals, including IEEE Transactions on Multimedia, Computer Vision and Image Understanding, Pattern Recognition Letters, Multimedia Systems, and Multimedia Tools and Applications. He is a member of the IEEE, the International Association for Pattern Recognition (IAPR), and the European Laboratory for Learning and Intelligent Systems (ELLIS).
He holds several scientific and institutional leadership roles in Artificial Intelligence and digital transformation. He is President of the Center for Informatics, Digitalization and Artificial Intelligence (CIDIA) at the University of Catania and Rector’s Delegate for “AI for Innovation.” He is also a member of the Academic Board of the Italian National Ph.D. Program in Artificial Intelligence – Health and Life Sciences area, and a member of the Board of Directors of the FAIR Foundation.
He has served and currently serves as Principal Investigator and coordinator of numerous national and international research and innovation projects in Artificial Intelligence and its applications, funded through European, national, and regional programs. His most recent projects include AI4NATURE, SINTESI, and TOGETHER, alongside several research and technology-transfer initiatives developed in collaboration with universities, research organizations, public institutions, and national and international companies.
He is the author of more than 300 scientific publications in international journals and conference proceedings, including numerous papers published in leading international Computer Vision and Machine Learning venues, such as IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), International Journal of Computer Vision (IJCV), Computer Vision and Image Understanding (CVIU), NeurIPS, CVPR, ICCV, and ECCV.
List of all publications is available here.
Concetto Spampinato’s research activities primarily focus on Artificial Intelligence, Machine Learning, and Computer Vision, with particular emphasis on the development of Deep Learning models capable of learning, interpreting, and reasoning over visual and multimodal information. His main research lines include Continual Learning and learning models inspired by cognitive and neurobiological mechanisms, generative models and foundation models, multimodal and vision-language learning, Explainable AI, and the development of methodologies for image and video identification, classification, segmentation, and understanding. A significant part of his research is also devoted to applying Artificial Intelligence to scientific and real-world problems, with particular emphasis on biomedical imaging and Underwater Computer Vision. In the latter area, his research focuses on developing intelligent systems for the automated analysis of underwater images and videos, including the recognition, tracking, and counting of marine species, the estimation of their abundance and biomass, and, more broadly, the use of Computer Vision and foundation models for monitoring marine biodiversity and ecosystems.
For more detailed information, please visit https://scholar.google.it/citations?user=Xc2rx8j4O7UC&hl=en