KEYNOTE SPEAKERS

Giancarlo Fortino (IEEE Fellow)

Department of Informatics, Modeling, Electronics, and Systems Engineering, University of Calabria, Via P. Bucci, cubo41c, Rende, Cosenza (CS), Italy

Keynote Talk: Generative Digital Twins

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, Gener-ative 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 for maximizing the performance of DTs operating in the IoT domain and deployed in the edge-cloud continuum.


Finally, we illustrate the potential of a GDT in emblematic Smart City scenarios through a use case involving the prediction of vehicles' trajectories when, due to uncontrolled events, only partial information is accessible. The outlined solution conciliates accuracy and explainability in the trajectory prediction with overall system robustness and effectiveness.


Biography


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. Since 2016, he is senior research fellow at the Italian ICAR-CNR Institute. Fortino is also distinguished professor and scientist of several chinese universities and research centers: Wuhan University of Technology (WUT), Huazhong Agricultural University (HZAU), Huazhong University of Science and Technology (HUST), Nanyang Institute of Technology (NYIST), East China Jiaotong University (ECJTU), Chengdu University of Information Technology (CUIT), and Shenzhen Institute of Advanced Technology. He was also visiting researcher at ICSI, Berkeley (USA), in 1997 and 1999 and visiting professor at Queensland University of technology in 2009. He is currently Distinguished Lecturer of the IEEE SMC society. At Unical, he is the Rector’s delegate to Int’l relations, the chair of the PhD School in ICT, the director of the Postgraduate Master course in INTER-IoT, and the director of the SPEME lab as well as co-chair of Joint labs on IoT established between Unical and WUT, SMU and HZAU Chinese universities, respectively. Fortino is currently the scientific responsible of the UNICAL Digital Health group of the Italian CINI National Laboratory and of IEEE at Unical initiative. He is Highly Cited Researcher 2020-2024 in Computer Science by Clarivate. He had 25+ highly cited papers in WoS, and h-index=86 with 30000+ citations in Google Scholar. His research interests include wearable computing systems, e-Health, Internet of Things, agent-based computing, and, more recently, generative AI in Education. He is author of 750+ 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, Information Fusion, EAAI, etc. He chaired many int’l workshops and conferences (about 150), was involved in a huge number of int’l conferences/workshops (about 1000) as IPC member, is/was guest-editor of many special issues (about 100). 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 currently Associate VP of the Cybernetics area of the IEEE SMCS and former member of the IEEE SMCS BoG and former chair of the IEEE SMCS Italian Chapter.

Tadahiko Murata (IEEE Fellow)

D3 Center, and Graduate School of Information Science and Technology, The University of Osaka, Osaka, Japan

Keynote Talk: Future of Digital Twin: How to Consider Human Factors in Cybernetics

In real-scale social simulations using digital twins, considering "human factors"—the people living and working within target communities—is essential. Attributes such as age, sex, and education level (individual background), along with household composition and economic status (household context), are critical parameters that govern individual decision-making. However, as these are highly sensitive private data, they remain largely inaccessible to researchers.

This presentation addresses this accessibility challenge through "Data Synthesis." I will detail the methodology for constructing "Synthetic Population Data" on a national scale (e.g., the entire population of Japan) while strictly preserving privacy by maintaining the statistical characteristics of official statistics. Applications in real-scale social simulations will also be presented.
Furthermore, from a cybernetic perspective, I propose a next-generation paradigm that integrates psychological traits (Big Five) into these synthetic populations. By treating decision-making models—where individual backgrounds, household contexts, and psychological traits intricately intertwine—as a "prism" and high-precision synthetic data as a "light source," we can refract and validate a vibrant spectrum of "What-if" scenarios. Through this presentation, I would like to encourage research communities to co-create a new engineering evaluation framework that harmonizes individual temporal consistency with societal diversity, transcending conventional metrics.


Biography


Tadahiko Murata (IEEE Fellow) received his B.S., M.S., and Ph.D. degrees from Osaka Prefecture University, Osaka, Japan, in 1994, 1996, and 1997, respectively. He began his academic career at the Ashikaga Institute of Technology in 1997, before joining the Department of Informatics at Kansai University in 2001, where he was appointed Full Professor in 2009.


In 2023, he joined The University of Osaka as a Full Professor at the Cybermedia Center, Currently renamed as D3 Center, a premier research hub dedicated to Digital Design, Datability, and Decision Intelligence. Based on high-performance supercomputing infrastructure, the center serves as a critical engine for data-driven innovation in both academic and public sectors. He currently leads advanced research and provides instruction at the Graduate School of Information Science and Technology and the School of Engineering. His research interests encompass multi-objective optimization, large-scale social simulations, human-centric digital twins, and high-performance computing.

As a leader in his field, Dr. Murata is currently serving as an IEEE SMC Society Distinguished Lecturer (2024–2026). He has held numerous leadership roles, including Vice President for Organization and Planning(2022–2025) and a member of the Board of Governors (2015–2020) for the IEEE SMC Society. He also served as President of the Japanese Society for Evolutionary Computation (2020–2022) and Vice President of the Japan Society for Fuzzy Theory and Intelligent Informatics (2023–2025).

Guanghui Wen

School of Automation, Southeast University, Nanjing, China

Keynote Talk: Distributed Consensus and Optimization of Multi-Agent Systems with Switching Communication Topologies

In recent years, the development of perception, communication, and embedded technologies has had a transformative impact on system analysis and control methods. Modern engineering control systems are increasingly characterized by networked structures and intelligent units. Within this context, the concept of multi-agent systems (MASs) has emerged, and the distributed cooperative control and optimization of such systems have gradually become a research frontier in the fields of systems and control. In practice, due to factors such as the limited communication range of agents and interference in communication links, the communication topology of MASs often exhibits dynamic switching characteristics. This talk begins by discussing the key issues of consensus control in MASs under switching communication topologies, outlining the critical techniques for addressing these problems: the common Lyapunov function method and the multiple Lyapunov function method. For MASs with directed switching communication topologies, it presents the construction methods and consensus criteria of multiple Lyapunov functions based on nonsingular M-matrix theory, and further explores low-conservatism multiple Lyapunov function construction methods based on Lyapunov inequalities and optimization techniques. On this basis, the robust optimization problems of MASs with physical dynamics under switching communication topologies are discussed. Finally, the application of the related theoretical results in the formation control of unmanned surface vessels is shared, along with personal insights on related emerging research topics.


Biography


Guanghui Wen received his Ph.D. degree in Mechanical Systems and Control from Peking University, Beijing, China, in 2012. He is currently an Endowed Chair Professor and the Vice Dean of the School of Automation at Southeast University, Nanjing, China. His current research interests include coordination control of autonomous intelligent systems, analysis and synthesis of complex networks, cyber-physical systems, resilient control, and distributed reinforcement learning. He has published more than 300 papers, including more than 200 publications in top-tier journals in the fields of systems and control (TAC, Automatica, etc.), as well as CCF-A conferences including AAAI, IJCAI. Among them, one paper was published in Nature Reviews Electrical Engineering. Prof. Wen was the recipient of the National Science Fund for Distinguished Young Scholars, the China Youth Science and Technology Award, the Australian Research Council Discovery Early Career Researcher Award, and the Asia Pacific Neural Network Society Young Researcher Award. He currently serves as a Technical Editor of the IEEE/ASME Transactions on Mechatronics and an Associate Editor of the IEEE Transactions on Industrial Informatics, the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Intelligent Vehicles, the IEEE Transactions on Fuzzy Systems, the IEEE Transactions on Systems, Man and Cybernetics: Systems, the IEEE Open Journal of the Industrial Electronics Society, and the Asian Journal of Control. Prof. Wen has been recognized as a Highly Cited Researcher by Clarivate from 2018 through 2024. He is an IET Fellow.

Bo Qi


Biography


Bo Qi is an Associate Professor with the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. He received his B.S. degree in Mathematics from Shandong University, Jinan, China, in 2004, and his Ph.D. degree in Control Theory from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, in 2009. His current research focuses on quantum metrology, quantum machine learning, and quantum computing.