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 110 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, ESC4DRES, EdgeAI, EdgeAI-TRUST, etc..).
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.
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.
Guida alle tesi di laurea
Students interested in thesis or internship topics related to Deep Learning applications in the industrial/automotive/legal field, in collaboration with industry/companies, can contact me via email to schedule a f2f meeting in my office.
Current open topics:
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Embedding Methods and Dynamic Indexing for LLM Engines in Document-based RAG Mode;
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Intelligent FUOA (Firmware Update Over-the-Air) – Foundation models and intelligent systems for automatic code/patch generation to monitor ECUs in next-generation electric vehicles;
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Hybrid Approaches Based on Perceptual Deep Learning and Foundation Models for Agrifood Applications – Focus on local-satellite monitoring of soil evapotranspiration indices, water consumption, yield, etc.;
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Implementation of a Fully Local LLM Engine with Custom Embeddings and Indexing for Business Process Support and Data-Center Monitoring;
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Implementation of an AI-based Framework Leveraging Foundation Models and Domain Adaptation Methods;
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Hyperbolic manifolds in Knowledge Distillation of AI architectures over resource-constrained microcontrollers;
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Neuro-Modulation of Deep Learning Algorithms within Hierarchical Structure;