Carmelo Fabio Longo
Ph.D, Chief Information Security Officer (CISO)
University Hospital Policlinico of Catania (Italy)
Ph.D, Chief Information Security Officer (CISO)
University Hospital Policlinico of Catania (Italy)
Carmelo Fabio Longo currently serves as Chief Information Security Officer (CISO) at the University Hospital Policlinico of Catania (Italy), where he is responsible for cybersecurity strategy, information security governance, risk management, and the protection of critical healthcare IT infrastructures. After graduating in Computer Science in 2000, he began his professional career in healthcare IT engineering, gaining extensive experience in the design, management, and security of complex information systems. Since 2017, he has also been involved in applied research on Artificial Intelligence, robotic and multi-agent systems, with a particular focus on natural language processing and cognitive architectures.
In 2022, he obtained his Ph.D. in Computer Science from the University of Catania. He subsequently worked as a researcher at the Institute of Cognitive Sciences and Technologies of the Italian National Research Council (CNR-ISTC), conducting research on artificial cognitive systems, semantic knowledge representation, and intelligent agents.
His main research interests include Artificial Intelligence, Cognitive Architectures, Natural Language Processing, Multi-Agent Systems and Semantic Web.
Scientific curricula: https://www.dropbox.com/scl/fi/vltrl4b2pg7cqm7pxfuq7/Curricula.pdf?rlkey=btfj7vsoyo4phnudmdyc2ugg1&dl=0
A privacy-preserving chatbot framework leveraging open-source large language models for patient simulation in psychotherapy training.
The article “A privacy-preserving chatbot framework leveraging open-source large language models for patient simulation in psychotherapy training”, published in Health Psychology Research in September 2026, introduces Psy-Mimic, an innovative platform leveraging open-source Large Language Models (LLMs) to simulate virtual patients for psychotherapy training.
The framework aims to provide psychotherapy trainees with a safe, controlled, and realistic environment in which to practice and refine their clinical skills through interactive conversations with AI-generated patients. A key distinguishing feature is its strong emphasis on privacy preservation, enabling local deployment without relying on external cloud services.
Developed through an interdisciplinary collaboration combining artificial intelligence, computer science, and clinical psychology, Psy-Mimic represents a promising step toward scientifically validated AI-driven psychotherapy training tools, laying the groundwork for future advances in computational patient simulation, behavioral realism, and multimodal interaction.
Paper: https://healthpr.org/journal/HPR/articles/online_first/9211
Sezione di Psicologia Clinica e Dinamica
17-19 Settemebre 2026
Joyful moments from the presentation of Psy-Mimic by the validation team led by Prof. Pasquale Caponnetto. The Congress provided a valuable opportunity for the team to engage directly with mental health professionals, fostering insightful discussions on the platform’s effectiveness and potential applications in psychotherapy training. The event was a great success, enabling the team to gather valuable feedback, exchange perspectives, and identify promising directions for future developments and improvements of the Psy-Mimic platform.
Pictured from left to right (upper picture): Dr. Giulia Schilirò, Dr. Noemi Vitale, and Dr. Alessia Sangiorgio, all members of the Psy-Mimic team, whose contributions are instrumental to the project's validation and ongoing development.
AI for Social Sciences, Humanities, and Heritage Science
National Research Council (Rome)
In March 2026, as a result of the scientific collaboration between the Institute of Cognitive Sciences and Technologies of the Italian National Research Council (CNR-ISTC) and the University of Catania, the Psy-Mimic project was presented, under the title “A Privacy-Preserving Chatbot Framework Leveraging Open-Source Large Language Models for Patient Simulation in Psychotherapy Training.”
Psy-Mimic demo: https://youtu.be/EG2mdgmjb10
The future of Psy-Mimic: https://youtu.be/L3J1_4EdZn8
October 2025 was quite productive for the StLab group of CNR-ISTC:
The paper presents a novel fine-tuning approach for LLMs named EXAR (EXclusive AutoRegressive fine-tuning) aimed at injecting metaknowledge about a particular task (in this case Question-Answering) in order to improve or self-correct previous errors.
Title: Eliciting metaknowledge in Large Language Models
Abstract:
The introduction of Large Language Models (LLMs) able to exhibit a number of linguistic and extra-linguistic capabilities has represented, in the last years, one of the main frontiers in Artificial Intelligence (AI) research. Researcher from various disciplines debate about whether or not, among the capabilities of LLMs, there is the one of using knowledge about knowledge – usually considered one of the antechambers of meta-cognition in cognitive agents – about a particular task in order to improve or self-correct previous errors. In this work we propose a novel fine-tuning approach for LLMs, named EXAR, based on a multi-stage process leveraging past predictions from an early version of the same, and aimed at injecting metacognitive features for the task of Question-Answering. The conducted experiments on Llama-2-7B-chat showed promising improvements on the quality of the outcomes, due to the fact that the LLM acquired the ability to detect its own wrong predictions forcing itself to repeat submissions, thorough a prompt designed to fix inadmissible predictions, whenever detected. Such detection is achieved by enquiring the same LLM acting as meta-validator, through another prompt specifically designed for such purpose.
Among LLMs meta-cognitive techniques based on chain-of-thoughs, here we investigated whether metaknowledge can be injected by fine-tuning in an auto-regressive fashion-way based on past inferences.
In June 17-19, 2024, the Conference Advance in Cognitive Systems held in Palermo, where, together with Misael Mongiovì, Luana Bulla and Antonio Lieto, I submitted a paper titled: "Eliciting Metaknowledge in Large Language Models". The contribution investigated on improving predictions on Large Language Models, through a self-corrective inference taking in account of past inferences, which can be considered also kind of Metacognition. The work was accepted as poster and presented by Antonio Lieto.
The Eleventh Annual Conference held in Palermo (Italy), June 17-19, 2024
Eliciting Metaknowledge in Large Language Models
In the 13th International Conference on Data Science, Techonology and Applications, the organization impressed me for the capillary communication for every aspect of the conference, the keynotes were interesting and I met several scholars with whom there could be future collaborations. For the occasion I presented an article on hierarchical classification of texts, in the absence of training datasets, using the Large Language Model Llama2 for the generation of synthetic texts.
The conference was featured by Jurgen Schmidhuber keynote, who can be considered the father of the pre-transformers state-of-the-art architecture known as Long Short Term Memory (LSTM)
Proud of achieving the Best Paper Award at DATA 2024 held in Dijon.
HTC-GEN: A Generative LLM-Based Approach to Handle Data Scarcity in Hierarchical Text Classification
14th International Conference on Formal Ontology in Information Systems
Last conference day
Together with Marco Monti and John Beverley