Francesco RUNDO
Francesco Rundo received his M.Sc. degree in Computer Science Engineering and a Ph.D. in Applied Mathematics for Technology from the University of Catania, Italy. He previously worked as a Senior Technical Staff Researcher at the R&D Division of STMicroelectronics, Catania. Currently, he is a Tenure-Track Researcher and Assistant Professor at the Department of Mathematics and Computer Science, University of Catania.
He has obtained the National Scientific Qualification (ASN) for Associate Professor in Computer Science (SSD 01/B1).
He has obtained the National Scientific Qualification (ASN) for Full Professor in Computer Science Engineering (SSD 09/H1).
He is an Associate Editor of the IEEE Open Journal of the Computer Society.
He has co-authored over 120 scientific contributions and several international patents in the field of Deep Learning based applications.
He has extensive experience in national and european-funded research projects in the field of AI-driven solutions (ADAS+, NEUROKIT2E, R-PODID, ARCHIMEDES, REACTION, EdgeAI, etc..).
He is Principal Investigator (for University of Catania) for the EU Funded Project "NeAIxt" (Next Generation of edge AI crossing technology fields) - HORIZON-JU-Chips-2024-1-IA ; Prop Nr. 101194172.
He served as Co-organizer and/or Program Chair of several workshops in conjuction of key AI/Computer Vision conferences such as CVPR, ICCV, ECCV. He is a member of the Computer Science Ph.D. Scientific Board at the Department of Mathematics and Computer Science, University of Catania. He is also a member of the National Artificial Intelligence Ph.D. Scientific Board - University "Campus Bio-Medico" of Rome.
He is a member of the research group at the Department of Mathematics and Computer Science called IPLAB (Image Processing Laboratory).
He is a member of the Italian Association for Research in Computer Vision, Pattern Recognition and Machine Learning (CVPL).
He is the scientific coordinator of the research activity: “AI4Industry, Legal and Financial Applications.”
His main research interests include: Bio-inspired computational models, Advanced Deep learning in non-conventional geometric spaces, Perceptive Deep Learning in Industrial/Automotive applications, GenerativeAI, Medical Imaging, Neuro-Modulation in Continual Learning framework.
Main research interests: Bio-inspired computational models, Advanced Deep learning in non-conventional geometric spaces, Perceptive Deep Learning in Industrial/Automotive applications, GenerativeAI, Medical Imaging, Neuro-Modulation in Continual Learning framework.