Prof. Giancarlo Fortino (IEEE Fellow), University of Calabria, Italy 

Giancarlo Fortino (IEEE Fellow 2022) is Full Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a PhD in Computer Engineering from Unical in 2000. He is also distinguished professor at Wuhan University of Technology (China), high-end expert of many chinese universities, including Huazhong University of Science and Technology, South China University of Technology, Shanghai Maritime University, etc., senior research fellow at the Italian ICAR-CNR Institute, CAS PIFI Group international fellow at SIAT (Shenzhen), and Distinguished Lecturer for IEEE Sensors Council, SMC society, and IoT TC. He was also visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999 and visiting professor at Queensland University of technology in 2009. At Unical, he is the chair of the PhD School in ICT, the director of the Postgraduate Master course in AI-driven Radiomics, and the director of the SPEME lab, the Radioamica lab, as well as co-chair of Joint labs on IoT established between Unical and WUT, SMU and HZAU Chinese universities, respectively, and a Joint lab on AI-driven Robotics established with indian istitutions. Fortino is currently the scientific responsible of the Unical group of the Italian CINI National Laboratory of Digital Health and of the Unical group of the CINI Cyber Humanities WG. He is Highly Cited Researcher 2020-2025 in Computer Science by Clarivate (the only Italian professor currently ranked). He had 25+ highly cited papers in WoS, and h-index=91 with 36K+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, and agent-based computing. He is author of 800+ papers in int’l journals, conferences and books. He is (founding) series editor of IEEE Press Book Series on Human-Machine Systems and EiC of Springer Internet of Things series and AE of premier int’l journals such as IEEE TASE (senior editor), IEEE TAFFC-CS, IEEE THMS, IEEE T-AI, IEEE SJ, IEEE JBHI, IEEE OJEMB, IEEE OJCS, Information Fusion, BDCC, etc. He chaired many int’l workshops and conferences (140+), was involved in a huge number of int’l conferences/workshops (800+) as IPC member, is/was guest-editor of many special issues (80+). He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems, and recently cofounder and vice-CEO of the spin-off Bigtech S.r.l, focused on big data, AI and IoT technologies. Fortino is the VP of Cybernetics (term 2026-2027) of the IEEE SMCS, member of the IEEE SMCS ExCom, and former chair of the IEEE SMCS Italian Chapter.

Title: Generative Digital Twins: Principles, Architecture, Methodology, and Applications

Abstract: Digital Twins (DTs) are software replicas that not only mirrors physical entities but can also proactively predict, control, optimize and simulate their behavior. Born in the manufacturing sector, this concept after an initial hype stayed untouched for decades. The rise of Internet of Things (IoT) and Artificial Intelligence (AI) enabled DT, respectively, to exchange real-world data and to fully exploit it for fulfilling its own goals. Very recently, Generative AI (Gen-AI) methods started being sporadically applied to DT in different contexts and with different targets. In this talk, starting from our experiences on design, implementation and evaluation of DTs and, more recently, of Opportunistic DTs, we first provide a definition for the Generative DT (GDT) which embraces main distinctive aspects and potential of current and future Gen-Al-aided DTs. In particular, we disclose the role of Gen-AI in conciliating the model- and the data-driven approach for the development of DTs. Then, we analyze the added value of main Gen-AI architectures and development methodologies for maximizing the effectiveness and the performance of DTs operating in the IoT domain and deployed in the device-edge-cloud continuum. Finally, we illustrate the potential of GDT in emblematic use cases in the Smart City, Smart Manufacturing, Smart Water Systems, Smart Robotics, Smart Education and, more in general, in Smart IoT-driven domains.

王龙，北京大学系统与控制研究中心主任/教授，长江学者

Prof. Long Wang, Peking University, China

王龙，出生于中国西安。1986年获清华大学自动控制专业学士学位，1992年获北京大学动力学与控制专业博士学位。他曾先后在加拿大多伦多大学（1993年，与Bruce A. Francis教授合作）和位于德国慕尼黑的德国航空航天中心（1995-1997年，与Juergen E. Ackermann教授合作）从事研究工作。现任动力学与控制领域长江学者特聘教授、北京大学系统与控制研究中心主任。主要研究领域包括复杂网络化系统、演化博弈动力学、人工智能和仿生机器人。王龙教授曾于1999年和2017年两次荣获国家自然科学奖，并获得关肇直控制理论奖、张嗣瀛决策与控制奖，以及《控制理论与应用》《中国科学：信息科学》等期刊的优秀论文奖。他已指导50余名博士研究生，并曾多次在中国控制会议、中国系统科学大会、中国智能自动化会议等重要学术会议上作大会报告。

题目：从演化博弈到综合集成智慧

摘要：群体智能源于个体之间的局部相互作用与适应性行为更新，表现为一种从分布式行为中涌现出的认知能力。演化博弈论为刻画从微观决策到宏观秩序的演进过程提供了统一的分析框架。本报告首先介绍博弈论的基本概念，指出完全理性假设的局限性，并阐述有限理性条件下的演化视角；随后讨论复制子方程以及结构化群体中的演化动力学。在此基础上，本报告将综述运用演化博弈论研究群体智能的两类代表性工作：第一类侧重于构建群体估计任务模型，以刻画群体智能的涌现机制；第二类侧重于激励机制设计，探讨如何通过调整支付结构，有效提升群体在预测任务中的整体表现。最后，本报告将总结演化博弈论在连接微观行为与宏观结果方面的优势，并展望未来的研究方向。

Long Wang was born in Xi'an, China. He received the bachelor's degree in automatic control from Tsinghua University in 1986, and Ph.D. degree in Dynamics and Control from Peking University in 1992. He has held research positions at the University of Toronto, Canada (1993, with Professor Bruce A. Francis), and the German Aerospace Center, Munich, Germany (1995-1997, with Professor Juergen E. Ackermann). He is currently the Cheung-Kong Chair Professor of Dynamics and Control, and the Director of Center for Systems and Control of Peking University. His research interests include complex networked systems, evolutionary game dynamics, artificial intelligence, and bio-mimetic robotics. Prof. Wang was the recipient of the National Science Prize (twice in 1999 and 2017), Guan Zhaozhi Control Theory Award, Zhang Siying Award in Decision and Control, and the Best Paper Awards for journal publications in Control Theory and Applications, Science China Information Sciences, etc. He has supervised more than 50 Ph.D. students. He has also given a number of plenary lectures at major conferences, including Chinese Control Conference, Chinese Conference on Systems Science, Chinese Conference on Intelligent Automation, etc.

Title: From Evolutionary Games to Meta-synthetic Wisdoms

Abstract: Collective intelligence arises from local interactions and adaptive behavioral updates among individuals, manifesting as an emergent cognitive capability derived from distributed behaviors. Evolutionary game theory provides a unified analytical framework for characterizing the transition from microscopic decision-making to macroscopic order. This talk first introduces the fundamental concepts of game theory, highlighting the limitations of the full rationality assumption and presenting the evolutionary perspective under bounded rationality. We then discuss replicator equations and evolutionary dynamics in structured populations. Building on this foundation, this talk surveys two representative lines of research that employ evolutionary game theory to study collective intelligence. The first focuses on constructing models of collective estimation tasks to characterize how such intelligence emerges. The second emphasizes incentive design, exploring how adjustments to payoff structures can effectively enhance group performance in prediction tasks. Finally, the talk will summarize the advantages of evolutionary game theory in integrating microscopic behavior with macroscopic outcomes and outline directions for future research.