Researcher
luca.sabatucci@icar.cnr.it
CNR: National Research Council
Institute: ICAR (https://www.icar.cnr.it)
Palermo, Italy
Scholar: Google Scholar Profile
ORCID: 0000-0003-2852-9355
DBLP: DBLP Profile
PUBLONS: Publons Profile
Throughout my career, I have explored the synergies between Software Engineering and Artificial Intelligence, focusing on developing socio-technical systems. These complex systems integrate technological, human, and physical components, where interactions between people, technology, and the environment are fundamental to system functioning and performance.
My research journey has evolved through several key phases:
Ambient Assisted Living for the Elderly: Current focus on developing highly personalized services for elderly care, balancing system autonomy with user control and understanding. The research aims to integrate technology into elderly lives considering cultural, social, and aesthetic factors. This work involves collaboration with Karol srl, a healthcare facility serving as a test site for personalized assistance solutions.
Large Language Models in Requirements Engineering: Exploring the potential of Generative AI in requirements engineering, particularly in developing tools that can analyze textual documents, transcribe interviews, identify discrepancies between stakeholders, highlight areas needing clarity, and suggest questions to fill knowledge gaps. This research combines qualitative user-centered design approaches with advanced NLP capabilities.
A project focused on developing explainable, safe, knowledge-grounded, trustworthy, and bias-controlled language models. The project aims to create technology that can learn independently from limited data to support most European Union languages. Built on a sustainable computational framework, ELOQUENCE will serve as a guide for European citizens while reflecting European values and supporting safety-critical applications through human-in-the-loop approaches.
Development of a decision support technology platform based on analytical techniques and big data, aimed at improving the design, production, and distribution processes in the Fashion supply chain. The platform focuses on predictive-collaboration and risk-based management, incorporating forecasting capabilities to handle predictive errors and various risk factors. SMASH offers a modular approach allowing companies to configure their own instance based on specific requirements.