KEYNOTE SPEAKER I

Prof. Hamid Reza Karimi
MAE, IIAV Fellow, ISCM Fellow, AAIA Fellow, IEEE Senior Member
Milan Polytechnic University, Italy

 

Hamid Reza Karimi is a Professor of Applied Mechanics in the Department of Mechanical Engineering at Politecnico di Milano, Milan, Italy. Prof. Karimi’s original research and development achievements span a broad spectrum within the topic of automation/control systems, and intelligence systems with applications to complex systems such as vehicles, robotics and mechatronics. Prof. Karimi is an ordinary Member of Academia Europaea (MAE), Member of European Academy of Sciences and Arts (EASA), Member of The European Academy of Sciences (MEurASc), Member of Agder Academy of Science and Letters in Norway, Member of National Academy of Artificial Intelligence (NAAI), Honorary Academic Member of National Academy of Sciences of Bolivia, Distinguished Fellow of the International Institute of Acoustics and Vibration (IIAV), Fellow of The International Society for Condition Monitoring (ISCM), Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA), and also a member of the IFAC Technical Committee on Mechatronic Systems, the IFAC Technical Committee on Robust Control, the IFAC Technical Committee on Automotive Control, member of the board of Directors of The International Institute of Acoustics and Vibration (IIAV) and member of Management Committee of The International Society for Condition Monitoring (ISCM). Prof. Karimi is the recipient of the 2025 NAAI Distinguished Artificial Intelligence Scholar Award, the 2021 BINDT CM Innovation Award, the Web of Science Highly Cited Researcher in Engineering, August-Wilhelm-Scheer Visiting Professorship Award, JSPS (Japan Society for the Promotion of Science) Research Award, and Alexander-von-Humboldt-Stiftung research Award, for instance. Prof. Karimi serves/served as Editor-in-Chief and Book Series Editor for Springer, CRC Press and Elsevier. He has also participated as General Chair, keynote/plenary speaker, distinguished speaker or program chair for several international conferences in the areas of Control Systems, Robotics and Mechatronics. Prof. Karimi has served as vice-president for International Prize "Lombardia è ricerca" and is also the conference chair for the 2026 World Congress on Condition Monitoring (WCCM2026) to be held in Milan, Italy. Additionally, he is an Honorary Visiting Professor in the School of Computing & Engineering at the University of Huddersfield, UK.
Speech Title: Data-Driven Approaches to Active Noise Control Systems
Abstract: Active noise control (ANC) systems often operate in environments containing both deterministic and broadband noise, where traditional methods are limited by the need for prior acoustic measurements. This talk presents data-driven approaches using deep learning to predict acoustic pressure and capture spatial patterns in sound fields. It further explores the potential of intelligent ANC systems to address nonlinearities in electro-acoustic environments and improve broadband noise suppression. A direct control design framework based on near-field acoustic holography is introduced, enabling controller development without requiring detailed system models. The presentation concludes with practical insights into implementing effective ANC strategies for reducing noise transmission through encapsulated structures.

 

KEYNOTE SPEAKER II

Prof. Huawei Huang
IEEE Senior Member
Sun Yat-sen University, China

 

Huawei Huang is a Professor at the School of Software Engineering and a jointly appointed Professor at Lingnan College, Sun Yat-sen University. He is a recipient of the Guangdong Provincial Distinguished Young Scholars Fund and has been listed among the World’s Top 2% Scientists annually since 2021. His research interests include high-performance blockchain systems and protocols, blockchain-based financial protocols, trustworthy infrastructure for AI agents, and distributed computing and networking. His work has been published in CCF-A journals such as ToN, JSAC, TPDS, TSC, TDSC, TIFS, TMC, and TC, as well as CCF-A international conferences including WWW and INFOCOM. He has served as chair for more than ten forums, symposia, and workshops at academic conferences in China and abroad. He has led more than ten research projects, including a project under the National Key R&D Program of China, two General Program projects funded by the National Natural Science Foundation of China (NSFC), and the Guangdong Provincial Distinguished Young Scholars Program. He has published five English academic monographs, textbooks, and popular science books on blockchain. He led his team to develop and open-source the blockchain experimental platform BlockEmulator, which provides mature experimental methods, tools, and open-source code for blockchain researchers and has served researchers from nearly 100 countries and regions. He also led the development and launch of the academic version of the Blockchain “BrokerChain Testnet”.
Speech Title: AgentEmulator: A Blockchain-Empowered Testbed for Trustworthy AI Agent Infrastructure
Abstract: AI agents are evolving from chatbots into autonomous digital workers. But when they act, pay, and collaborate on their own, how can we know what they did, who is responsible, and whether their interactions are trustworthy? Blockchain acts as a shared, tamper-resistant ledger that can provide such trust. This talk introduces AgentEmulator, a blockchain-empowered testbed for trustworthy AI agent infrastructure, developed by Prof. Huang’s Research Group at Sun Yat-sen University (a.k.a. HuangLab). Built on the blockchain simulation tool BlockEmulator-X, AgentEmulator v1.0 lets researchers describe agent actions such as joining, paying, and leaving in a simple file. The system automatically turns these actions into on-chain transactions, records the results, and generates Agent’s account balance charts and visual reports. Under the same settings, experiments can be reproduced. AgentEmulator aims to be a practical sandbox for studying identity, auditing, payment, and collaboration mechanisms for future trustworthy AI agents.

 

KEYNOTE SPEAKER III

Prof. Lidong Yang
IEEE Member
The Hong Kong Polytechnic University, China

 

Lidong Yang is an Assistant Professor and PhD supervisor in the Department of Industrial and Systems Engineering at The Hong Kong Polytechnic University. He is a member of the Academic Committee of the PolyU-Peking University Third Hospital Joint Laboratory for Medical-Engineering Collaborative Innovation. His research focuses on microrobotics and systems. He received the 2026 PolyU Young Innovative Researcher Award, the 2025 Machines Young Investigator Award, the 2022 Hong Kong Young Scientist Award (Finalist), and the 2021 Chinese Association of Automation Outstanding Doctoral Dissertation Nomination Award. His research also received multiple Best Paper Awards at international conferences on robotics and automation. He has (co-)led projects funded by the NSFC, Hong Kong RGC, ang Guangdong Basic and Applied Basic Research Foundation. He serves as Associate Editor of IEEE Transactions on Robotics, Executive Editor-in-Chief of Advanced Clinical Engineering, Associate Editor of Robot Learning, and Youth Editor of SmartBot.
Speech Title: Machine learning for microrobot autonomy
Abstract: Microrobot technology has advanced rapidly, as its application scenarios expand, including targeted therapy and micro/nano manipulation. Traditional control methods, however, rely heavily on accurate physical models and manual parameter tuning. Under the microscale conditions imposed by Brownian motion, multiphysics coupling, and strong external disturbances, they suffer from limited accuracy in modeling, poor robustness, and weak adaptability. Machine learning techniques, with reinforcement learning as an example, offer a new technical pathway for overcoming these bottlenecks, thanks to their strong nonlinear mapping, self-learning, and environment-interaction capabilities. This talk first examines the paradigm shift that AI is bringing to robot control and analyzes the distinctive challenges that microrobot control faces in microscale environments. On this basis, I present our representative work on microrobot control, spanning both classical control methods and recent learning-based control approaches. Looking ahead, I will discuss future directions for learning-based control of microrobots.