The Semantic Analytics Group (SAG) is part of the Artificial Intelligence Group at the University of Roma Tor Vergata. The group investigates advanced methods in Natural Language Processing, Machine Learning, and Knowledge Representation, with a strong focus on Large Language Models, Generative AI, and Multimodal Systems.
Our research spans across fundamental and applied areas, including:
- LLM, Computer Vision and Multimodality, exploring how generative models and multimodal architectures (language, vision, and perception) can understand, reason, and interact in complex environments.
- Advanced Deep Learning, developing efficient and sustainable architectures, from kernel machines to transformer-based models, with special emphasis on prompt engineering, optimization strategies, and robustness in low-resource and multilingual settings.
- AI for Complex Scenarios, applying intelligent systems to medicine, genomics, epidemic intelligence, tourism, business process modeling, and nuclear knowledge integration, with a focus on explainability, ethical impact, and real-world applicability.
- Applied AI for Society and Industry, designing systems for brand reputation and sentiment analysis, open source intelligence, cultural heritage, and sustainability reporting, in close collaboration with industrial and institutional partners.
SAG advances trustworthy, explainable, and inclusive AI, integrating deep learning with structured knowledge and domain expertise to deliver robust solutions for dynamic, human-centric environments.
Overall, this site provides a comprehensive overview of the group’s activities. Visitors can explore the research themes and lines of investigation, the teaching portfolio at the University of Roma Tor Vergata — ranging from Artificial Intelligence to Machine Learning and Deep Learning — as well as the projects carried out in collaboration with academic, industrial, and institutional partners.
The site also features our open-source software tools and linguistic/multimodal resources, together with information about the people involved in the lab. In summary, it offers a broad perspective on SAG’s scientific contributions, educational engagement, and technology transfer initiatives.