Antonio Liotta

Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy

9:00-10:00

Making Data AI-Ready: why better data outperforms bigger models

Abstract: Advanced machine learning models rarely fail because of insufficient algorithmic sophistication, they fail because the data pipeline is not AI-ready. In large, heterogeneous, and imperfect datasets, bias, instability, and weak generalization are typically consequences of data design rather than model choice. This lecture presents a structured, data-centric methodology for transforming raw data into robust ML systems, emphasizing collection and representation, feature extraction, dimensionality reduction, bias mitigation, leakage prevention, and evaluation beyond accuracy. Using a music genre classification case study, we show how systematic data curation and iterative pipeline optimization can substantially improve generalization performance, often yielding higher return on investment than increasing model complexity. Participants will gain a practical framework for building scalable, reproducible, and resource-aware ML pipelines that extract maximal value from data before resorting to more complex models.

Short Bio: Antonio Liotta is Full Professor of Data Science and Machine Learning at the Faculty of Engineering, Free University of Bolzano (Italy), where he leads the Data-Driven Artificial Intelligence research area and co-directs the Master’s programme in Data Analytics for Economics and Management. He previously founded the Data Science Research Centre at the University of Derby. For more than three decades, his work has explored a central question: how can we transform complex, messy, real-world data into reliable and efficient intelligent systems? His research bridges data science, machine learning, and intelligent infrastructures, with applications spanning smart cities, Internet of Things, energy systems, artificial vision, and human-centred AI. He is widely recognized for pioneering contributions to micro-edge intelligence and sparse neural networks for embedded learning, advancing AI that is not only powerful, but scalable and sustainable. Professor Liotta has authored over 350 scientific publications and collaborates internationally across disciplines to advance data-centric AI. He is Editor-in-Chief of the Springer Internet of Things book series and co-author of the books Networks for Pervasive Services and Data Science and Internet of Things. His work continues to focus on building AI systems that create value from data in robust, responsible, and resource-aware ways.

Paolo Sorino

Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy

10:00-11:00

Artificial Intelligence and Explainable AI in Clinical Decision Support Systems: Innovation, Interpretability, and Trust

Short Bio: Paolo Sorino is a Post-Doctoral Researcher in Information Processing Systems (ING-INF/05) at the Polytechnic University of Bari. He received a PhD in Information Engineering from Politecnico di Bari, defending a thesis entitled “Leveraging Artificial Intelligence for Enhanced and Human-centred Healthcare Solutions”. His research focuses on the integration of Artificial Intelligence to develop innovative and human-centred healthcare solutions, as well as on the analysis and modeling of biological signals to improve the effectiveness and accessibility of healthcare services. His scientific interests include Machine Learning and Deep Learning for clinical prediction, Explainable AI (XAI), Brain-Computer Interfaces (BCI), and biomedical signal analysis (EEG/ECG). He is the author of more than 30 peer-reviewed publications and actively participates in national and European research projects on AI-driven healthcare systems.

Coffee Break

11:00-11:15

Domenico Lofù

Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy 

11:15-12:15

AI-Driven Conflict: The Cyber Warfare in the Age of Artificial Intelligence

Short Bio: Domenico Lofù (Member, IEEE) is an Assistant Professor at Polytechnic University of Bari, Italy. He obtained his Ph.D. in  Information Engineering from Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Italy, in December 2022. From 2019 to 2023, he worked as a Security & AI Researcher at the Innovation Lab (ILAB) of Exprivia S.p.A. in Italy. He has contributed to several European and national projects, including ECHO H2020, MISTRAL H2020 and ACSOC Digital Europe, where he has led Security and Infrastructure activities. He also serves on the TPC of several conferences and works on several EU projects. His main research interests lie in the application of Artificial Intelligence (AI) techniques and methods to the domains of Information Security & Privacy, the Internet of Things (IoT), Healthcare, and Brain-Computer Interfaces (BCIs).

Emanuele Bellini

Department of Humanities Studies, Roma Tre University , Roma, Italy

12:15-13:15

Hybrid threats in Digital Cultural Heritage 

Short Bio: Prof. Emanuele Bellini is an Associate Professor in Information Processing Systems at the University of Roma Tre and a Visiting Academic at the University of Cambridge, Department of Computer Science and Technology. His research spans Cyber Resilience, Human-Cyber-Physical Systems, Critical Infrastructure Protection, and Trust Computing. He is also the founder of the emerging field of Cyber Humanities, and his current research investigates the security and protection of Digital Cultural Assets, as well as the counteraction of Cyber Cognitive Operations targeting Cultural Heritage, as part of the broader challenge of safeguarding memory,  knowledge and meaning in the digital age. Prof. Bellini serves as Chair of the IEEE SMC Technical Committee on Cyber Humanities and Vice-Chair of the IEEE SMC Technical Committee on Homeland Security. He is also Founder and Co-Chair of the IEEE International Conference on Cyber Security and Resilience (IEEE-CSR) and the IEEE International Conference on Cyber Humanities (IEEE-CH). His work is based on a transdisciplinary approch fusing technical and humanistic perspectives oto address the challenges of resilience, trust and sustainability in complex socio-technical critical ecosystems.