Keynote Speakers

Francesco Flammini

IDSIA, Switzerland

Prof. Francesco Flammini received the Laurea (cum laude) in Computer Engineering (2003) and the Ph.D. in Computer Engineering (2006) from the University of Naples Federico II, Italy. He brings a rare combination of long-term industrial leadership and academic research in intelligent transportation systems, cyber-physical systems, and cybersecurity for safety-critical infrastructures. After completing his doctorate, he spent 15 years in public and private organizations— including Ansaldo STS (now Hitachi Rail) and IPZS (Italian State Mint and Polygraphic Institute)—leading large international programs in intelligent transportation, critical infrastructure protection, and cybersecurity, serving as technical leader and unit head. Since 2020, Prof. Flammini has been Full Professor of Computer Science (Cyber-Physical Systems) at Mälardalen University, Sweden, and Technical Manager of the EU-funded RAILS project on Artificial Intelligence for smart railways. He has also held academic leadership roles at multiple institutions, including as Professor of Trustworthy Autonomous Systems at SUPSI (Switzerland) with affiliation to IDSIA, where he led the Trustworthy Autonomous Systems research group and served as Program Director of the B.Sc. in Data Science and Artificial Intelligence. He previously served as Senior Lecturer and Chair of the Cyber-Physical Systems environment at Linnaeus University (Sweden), and as Adjunct Professor at several universities, including the University of Maryland Global Campus Europe. He is also Full Professor at the University of Florence, contributing to the Resilient Computing Lab and the Computer Science Ph.D. steering board. Prof. Flammini is an IEEE Senior Member and an active volunteer leader across IEEE societies, including the Intelligent Transportation Systems Society. He serves on the IEEE Systems, Man, and Cybernetics Society Board of Governors as Associate Vice President for Members and Student Activities and chairs the IEEE SMC Technical Committee on Homeland Security. He has also served as Vice-Chair of the IEEE Computer Society Italy Chapter. He is an IEEE Computer Society Distinguished Visitor and an ACM Distinguished Lecturer. He has (co)authored 200+ publications and has served in leadership roles (chair, invited speaker, steering/program committee member, editor) for 50+ international conferences, books, and journals. He has been PI/technical manager and WP/task leader in 15+ research projects (largely EU-funded) and also serves as an expert evaluator for research agencies. He has supervised 10+ Ph.D. students as primary advisor and co-supervised 20+.

Keynote Title: Towards Trustworthy Autonomous Systems: The Role of Modeling and Digital Twins for Safe Perception

Abstract: Trustworthy autonomy ultimately hinges on safe perception: autonomous decisions are only as reliable as the sensing and inference pipelines that support them, especially under disturbances, faults, and attacks, while meeting quantitative risk constraints typical of safety-critical domains. In this talk, trustworthy autonomy is framed as justifiable autonomy, i.e., the ability to sustain dependable service delivery as operating conditions change. The increasing adoption of AI/ML in perception brings domain-specific vulnerabilities (e.g., adversarial attacks and the accuracy–robustness trade-off) and makes Trustworthy and Explainable AI key design principles. The core message is that Model-Based Engineering and Digital Twins can provide a rigorous foundation to engineer and assure safe perception: Digital Twins are presented as predictive run-time models enabling continuous monitoring, planning, and safe reconfiguration, integrated into an autonomic MAPE-K loop and organized hierarchically across multiple system levels. A multi-sensor event-detection case study is discussed, combining redundant/heterogeneous sensor fusion with reputation mechanisms, supported by explainable probabilistic models such as Dynamic Bayesian Networks within the MAPE-K feedback loop. In the REXASI-PRO project, these concepts are applied to an assistive “wheelchair-drone” system for critical scenarios such as road crossing, highlighting how sensor fusion and autonomic adaptation improve robustness and safety of perception.