Simone Palazzo
DIEEI - Università di Catania, Italy
► Deep learning theory and application for astrophysics
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Simone Palazzo is Assistant Professor at Università degli Studi di Catania. His research interests include machine learning, artificial intelligence and pattern recognition, with particular focus on computer vision, medical imaging, explainable AI and bio-inspired methods for machine learning. He is author of 150+ peer-reviewed scientific papers, with a h-index of 27 and 3800+ citations. He took part of national (PO-FESR 2014/2020) and international (EU-FP7, H2020) projects, and is coordinator of national (PRIN PNRR 2022) project "RESILIENT". He is also Principal Investigator for University of Catania on Horizon project "ECS4DRES" |
Giovanni Bellitto
DIEEI - Università di Catania, Italy
► Deep learning theory and application for astrophysics
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Giovanni Bellitto received his PhD from the University of Catania in 2023. He has been serving as an Assistant Professor at the same University since 2024. He is a member of the PeRCeiVe Lab research group since 2019. His research interests include machine learning and artificial intelligence, with a particular focus on bio-inspired methods for incremental learning, federated learning, and medical image analysis. |
Federica Proietto Salanitri
DIEEI - Università di Catania, Italy
► Deep learning best practices in astrophysics
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I received my academic education at the University of Catania, where I achieved my PhD degree in 2023. Since January 2024, I'm an Assistant Professor at the Department of Electrical, Electronic, and Computer Engineering at the same institution. I am a member of the PeRCeiVe Lab research group, where I started my research activities in the areas of artificial intelligence, machine learning, and computer vision, with a particular focus on medical image analysis, federated learning, and explainable AI. My current research interests include developing AI-driven solutions for medical imaging, integrating federated learning frameworks for privacy-preserving data analysis, and applying explainable AI techniques to enhance transparency in machine learning models. I have co-authored several publications in peer-reviewed international journals and conferences and have contributed as a program committee member and cconferences in the fields of computer vision, AI, and machine learning. |
Matteo Pennisi
DIEEI - Università di Catania, Italy
► Deep learning best practices in astrophysics
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Matteo Pennisi is an Assistant Professor at the University of Catania, working within the Perceive Lab at the Department of Electrical, Electronic and Computer Engineering (DIEEI). He holds a Ph.D. in Artificial Intelligence and is actively engaged in national and international research collaborations. His research interests primarily include Federated Learning, Generative Models, and Continual Learning, with a particular focus on developing privacy-preserving and bio-inspired AI systems. More recently, his work has also expanded to Explainable AI and Federated Unlearning, with the goal of enhancing transparency and user control in decentralized learning environments. |
