Keynote Speakers

Short Biography

Domenico Talia is a full professor of computer engineering at the University of Calabria, Italy and an Honorary professor at Noida University, India. He is a co-founder of the start-up DtoK Lab. His research interests include Big Data analysis, artificial intelligence, high-performance computing, parallel and distributed machine learning, Cloud computing, social data analysis, and parallel programming models and languages. Domenico Talia has published 10 books and more than 400 papers in archival journals such as Communications of the ACM, IEEE TPDS, IEEE Computer, IEEE TKDE, IEEE TSE, IEEE TSMC-A, IEEE TSMC-B, IEEE Micro, ACM Computing Surveys, FGCS, Parallel Computing, IEEE Internet Computing, and highly reputed conference proceedings. He is a member of the editorial boards of IEEE Computer, ACM Computing Surveys, the Future Generation Computer Systems journal, and other archival journals. He served as a program chair or program committee member of many international conferences. Talia has been the recipient of the Euro-Par Achievement Award 2025. He is a senior member of the Association for Computing Machinery (ACM) and IEEE Computer Society and has been a reviewer for several research agencies and public administrations.

Keynote Title

Techniques for Exploiting Machine Learning and Explainable Artificial Intelligence in Healthcare

Keynote Abstract

Artificial intelligence techniques and systems are demonstrating their effectiveness in solving problems in many application areas, including healthcare. Over the years, several studies focused on leveraging machine learning and deep learning techniques to identify diseases in patients. However, while these techniques have demonstrated remarkable accuracy in diagnosis, they often operate as “black box” models, meaning they provide outputs without clear explanations of the rationale behind their decisions.

In response to this challenge, explainable artificial intelligence  (XAI) has emerged as a research area aiming at providing not only accurate diagnoses but also understandable and interpretable explanations for the decisions made by AI models.

For providing explanations Large Language Models (LLMs) may be exploited. This talk introduces and discusses cutting-edge XAI techniques for healthcare applications, which hold the promise of enhancing trust, enabling clinicians to better understand and contextualize AI results, and ultimately improving patient care. Some significant case studies are presented.