The aim is to make intelligent systems (video surveillance, environmental management and the circular economy, healthcare, etc.) more autonomous, as they are no longer expected to perform repetitive tasks alone, but to solve problems on their own, using machine learning and data fusion methods.

The aim of this area of research is to extract structured knowledge from structured and unstructured text. We are also exploring the fields of machine translation, automatic summarization, processing of legal, administrative and clinical texts, multimodal data extraction, opinion and sentiment analysis, and recommendation systems.

Specialized in AI, machine learning, big data processing, and natural language, our team conducts multiple research projects aimed at improving the expected outcome of the computer so that it can solve a problem by providing it with learning algorithms.

Privacy-preserving has become a major societal issue. In today’s connected world, more and more personal data is being collected or generated, often without the knowledge of the individuals concerned. The topics explored by our team include: distributed learning, federated learning, cryptography, secure computing (homomorphic encryption, trusted network, secure hardware, etc.).

Generative AI and large multimodal models (LMMs) such as large language models (LLMs) and large vision models (LVMs) are expanding technologies that are revolutionizing the way we work, live, collaborate and play. They can help us create new things, solve problems and better understand the world. In this research Team, we will focus on the impacts and advances of Generative AI (GenAI) and Agentic AI.