Claudio Sartori’s scientific activity spans several areas of artificial intelligence, data science, and advanced computational methods, with a strong focus on machine learning and intelligent data analysis. Earlier contributions were mainly centered on database systems, data mining, predictive analytics, outlier detection, and scalable algorithms for large datasets, reflecting a solid background in information systems and computational intelligence.
More recent research highlights a clear shift toward modern AI methodologies, especially natural language processing, representation learning, and quantum machine learning. A significant line of work concerns abstractive text and dialogue summarization, decoding strategies for natural language generation, and efficient transformer architectures for long-document summarization in low-resource settings. These studies indicate a strong interest in generative AI and language technologies.
Another prominent and rapidly growing research area is quantum artificial intelligence. Sartori has contributed to quantum neural networks, quantum support vector machines, hybrid quantum-classical learning frameworks, and quantum ensemble classification algorithms, exploring the integration of quantum computing techniques with machine learning models.
Additional recent work addresses knowledge-enhanced information retrieval, self-supervised and contrastive learning, knowledge graph embeddings, and named entity recognition with large and small language models.
Overall, the recent scientific interests converge on advanced AI systems that combine machine learning, language understanding, representation learning, and quantum computing, with particular attention to efficient and innovative computational paradigms for data-intensive and knowledge-driven applications.
Ongoing project: DARE - Digital Lifelong Prevention
Last project: Toreador (concluded)
