Prof. Daniele Ravì is an Associate Professor at the University of Messina, where he specializes in medical imaging, image-guided surgery, disease progression modeling, and smart sensing. He also holds an honorary appointment as an Associate Professor at University College London.
Dr. Ravì obtained his BSc and MSc in Computer Science, followed by a PhD in Computer Vision from the University of Catania. His academic training was further enriched by a visiting PhD year at the University of Surrey and postdoctoral appointments at Imperial College London and University College London. Beyond academia, he gained valuable industrial experience at STMicroelectronics and two technology startups.

He currently leads the AI-HealthLab research group, which pioneers advanced artificial intelligence methods for healthcare. His team was among the first to develop generative AI models—such as GANs and diffusion models—capable of simulating individual neurodegenerative trajectories directly from clinical brain MRI data. He has secured over £1.8M in research funding from bodies including Innovate UK and the Italian Ministry (FIS3), and has contributed to projects funded by the European Union and the EPSRC/Wellcome Trust focusing on developing and commercializing AI-driven pipelines for healthcare. His leadership has consistently delivered successful research and industrial projects through the coordination of multidisciplinary teams, effective resource management, and strategic stakeholder engagement.

Dr. Ravì is an active contributor to the scientific community, with over 20 journal articles in venues such as IEEE Transactions on Medical Imaging, Medical Image Analysis, and the IEEE Journal of Biomedical and Health Informatics, alongside numerous conference papers (e.g., MICCAI, MIDL, BSN, ICIP) and a patent. He serves as an Area Chair for leading conferences, including MICCAI and MIDL, and as an Associate Editor for Medical Image Analysis and Pattern Recognition. He also supervises PhD students, fosters industry collaborations, and teaches AI-related modules. His research has been nominated for a Best Paper Award at MICCAI and was a runner-up for the prestigious MedIA–MICCAI special issue.

For a complete list of publications, visit the Google Scholar profile.