Title: Frontiers of Intelligent Automation

Abstract: Over the past two decades, we have witnessed profound technological advancements fueled by disruptive innovations in both software and hardware. These changes have been accompanied by a convergence of information, communication, and control technologies, driving what is now known as digital transformation—the integration of digital technology into all areas of business. This transformation has fundamentally altered how organizations operate, interact, and deliver value. In manucatoring environments, digital transformation increasingly focuses on intelligent automation and manufacturing, where technology and machine intelligence combine to create more efficient, adaptable, and innovative processes. A significant recent development is the integration of Artificial Intelligence (AI), which has emerged as the primary enabler and facilitator. The study of Artificial Intelligence (AI) has been an enduring endeavor for scientists and engineers for over 65 years. The fundamental premise is that machines created by humans can go beyond AI has long captivated researchers, reflecting two intrinsic human traits: the relentless drive for higher productivity and an innate curiosity to understand and reshape the world. Despite experiencing periods of stagnation, often referred to as 'AI winters,' the technology has matured into a leading force. The presentation concludes with a discussion on the role of digital twins in industry. A digital twin (DT) is a dynamic virtual replica of an object, entity, or system that continuously synchronizes with data from its physical counterpart. It is developed and utilized throughout the entire life cycle, spanning from inception to retirement, effectively serving as a digital platform for testing, designing, and modifying real-world objects without direct interaction.

Bio: Okyay Kaynak received the B.Sc. degree with first-class honors and Ph.D. degrees in electronic and electrical engineering from the University of Birmingham, UK, in 1969 and 1972, respectively. From 1972 to 1979, he held various positions within the industry. In 1979, Bogazici University, Istanbul, Turkey. He is currently a Professor Emeritus of this university. He has held long-term (near to or more than a year) Visiting Professor/Scholar positions at various institutions in Japan, Germany, the U.S., Singapore, and China. His current research interests are in the broad field of intelligent systems. He has authored three books, edited five, and authored or co-authored more than 450 papers that have appeared in various journals and conference proceedings. Dr. Kaynak has served as the Editor in Chief of IEEE Trans. on Industrial Informatics, IEEE/ASME Trans. on Mechatronics and as the Co-Editor in Chief of IEEE Trans. on Industrial Electronics. He presently serves as the Founding Editor-in-Chief of IEEE Transactions on Industrial Cyber Physical Systems. Additionally, he is on the Editorial or Advisory Boards of several scholarly journals. He has received the Chinese Government’s Friendship Award, Humboldt Research Prize, Doctor Honoris Causa, Obuda University, Hungary, Fellowship of Asian Control Systems Association (2024), and the Academy Prize of the Turkish Academy of Sciences (2020). He is a member of this Academy. Most recently, he was cited as the Fellow of Asia-Pacific Artificial Intelligence Association, the Fellow of Industry Academy within the International Artificial Intelligence Industry Alliance.

Title: Advancing AI-enhanced Sensing Technologies for Human-Robot Interaction

Abstract:  Smart sensors are revolutionizing healthcare, human–machine interaction, and environmental monitoring by offering innovative solutions for real‑world challenges. This lecture will highlight our team’s recent advancements in smart, flexible, wearable sensor technologies, focusing on novel designs, enhanced performance, and wide‑ranging applications. By integrating advanced materials with AI‑driven algorithms, these sensors exhibit exceptional sensitivity, adaptability, and functionality, making them ideal for applications such as sign‑language recognition, human–robot interfaces, and physiological signal monitoring. Key developments discussed in this lecture include: (1) biometric‑tuned e‑skin sensors with real fingerprints that simultaneously detect pressure and vibration, enabling texture recognition and demonstrating that fingerprint topography substantially affects vibration transmission across the skin surface; (2) epidermal sensors made from electronic‑slime that offer exceptional flexibility, self‑healing capabilities, and high precision in motion recognition, ensuring seamless integration with the human body; (3) nanoparticle‑based e‑skin sensors that enable facial expression detection and advanced human–robot interaction; and (4) multi‑modal single‑surface sensors that integrate force, surface‑vibration, and temperature sensing for robotic grasping and object recognition. These advancements underscore the transformative potential of smart sensing technologies to address contemporary challenges in human–robot interaction and beyond. By leveraging cutting‑edge materials, intelligent algorithms, and innovative designs, flexible and wearable sensors are paving the way for smarter, more adaptable solutions that enhance human–robot interaction across diverse fields.

Bio: Wen Jung Li is Chair Professor of Mechanical Engineering and Vice‑President (Talent and International Strategy) at City University of Hong Kong (CityUHK). Before joining CityUHK in 2011, he was at The Chinese University of Hong Kong, where he directed the Centre for Micro and Nano Systems, and earlier held R&D positions at NASA/Caltech Jet Propulsion Laboratory, The Aerospace Corporation, and Silicon Microstructures in California. His group has advanced micro/nano sensors, micro/nano robotics, and AI‑ sensors, with publications in Nature Machine Intelligence, Nature Methods, Nature Communications, Science Advances, Advanced Science, and IEEE Internet of Things Journal. He is a Fellow of IEEE, ASME, and AAIA; a Chinese Academy of Sciences “100 Talents” awardee; and an elected member of the U.S. National Academy of Artificial Intelligence (NAAI). He served as President of the IEEE Nanotechnology Council (2016–17) and as founding Editor‑in‑Chief of IEEE Nanotechnology Magazine and the IEEE Open Journal on Nanotechnology. Holder of over 20 US/China patents, he has supported four student‑founded startups that commercialized intelligent sensing products for sectors ranging from aerospace to sports, winning awards including the Gold Medal at Inventions Geneva (2021), Best Innovative Technology Project at the China Hi‑Tech Fair (2020), and Gold at the 2018 World IoT Expo. Current research interests include bio‑MEMS, super‑resolution microscopy, and intelligent cyber‑physical sensors. He earned a B.S. and M.S. from USC and a Ph.D. from UCLA in Aerospace Engineering.

Title: A Review of LAB, Tracking Control Design Technique

Abstract. In this lecture, a review of LAB is presented, being compared with other techniques like Feedback Linearization and Backstepping. Some new issues about performance and implementation are discussed and some applications are outlined.

Bio: Pedro Albertos is an Emeritus Professor at the Department of Systems Engineering and Control, Universitat Politècnica de València, Spain. He is Doctor Honoris-Causa from Oulu University, Finland and Bucharest Polytechnic, Rumania, Honorary Professor at NorthEastern University, Shenyang, China. Invited professors in more than 20 Universities, he delivered seminars in more than 30 universities and research centres and more than 25 Plenary talks at IFAC/IEEE conferences. Authored over 300 papers, book chapters and congress communications, co-editor of 7 books and co-author of Multivariable Control Systems (Springer 2004), Feedback and Control for Everyone (Springer 2010), which received the Harold Chesnut Best Textbook Award at the IFAC World Congress in Toulouse in 2017, and Linear Algebra Control (Springer 2020). His research areas include embedded control systems, time delays systems and control applications. He is a “Prometeo” researcher in Spain and Ecuador, and was Associated editor of Control Engineering Practice, Automatica and Editor-in-Chief of the Spanish journal RIAI. Being IFAC President (1999-2002), he organized the XV IFAC World Congress (Barcelona, Spain). He is IEEE Life Senior Member and IFAC Fellow.

Title: Evolutionary Machine Learning: 70 Years of Progress

Abstract: Evolutionary machine learning have been very popular over the recent years. In this talk, I will firstly provide a brief overview of the history of evolutionary machine learning with the major developments over the past 50 years, then visit the main paradigms of evolutionary machine learning and their successes in classification, feature selection, regression, clustering, computer vision and image analysis, scheduling and combinatorial optimisation, deep learning, transfer learning and XAI/XML, and generative AI. The main applications, challenges and lessons as well as potential opportunities will be also discussed.

Bio: Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of Engineering New Zealand, a Fellow of IEEE, an IEEE Distinguished Lecturer, Professor of Computer Science (Artificial Intelligence) at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation and Machine Learning Research Group. He is also the Director of the Centre for Data Science and Artificial Intelligence at the University.

His research is mainly focused on AI, machine learning and big data, particularly in evolutionary learning and optimisation, feature selection/construction, computer vision and image analysis, scheduling and combinatorial optimisation, classification with unbalanced data and missing data, and evolutionary deep learning and transfer learning. Prof Zhang has published over 900 research papers in refereed international journals and conferences. He has been serving as an associated editor for over ten international journals and involving major AI and EC conferences as a chair. He received the “Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe 2023” and the “2024 Australasian Artificial Intelligence Distinguished Research Contribution Award”. He is also a Clarivate Reveals Highly Cited Researcher. Since 2007, he has been listed as a top five (currently No. 3) world genetic programming researchers by the GP bibliography.

Prof Zhang is the Chair for IEEE CIS Awards Committee. He is also a past Chair of the IEEE CIS Intelligent Systems Applications Technical Committee, the Emergent Technologies Technical Committee and the Evolutionary Computation Technical Committee, a past Chair for IEEE CIS PubsCom Strategic Planning subcommittee, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.

Title: Optimal Control Computation: Control Parametrization and Chebyshev Approximation

Abstract:This talk presents two computational methods for solving constrained optimal control problems: control parametrization and Chebyshev approximation. For control parametrization method, it discretizes the control functions, transforming the original constrained optimal control problem into a sequence of tractable, finitedimensional nonlinear optimization problems. The switching time optimization is used as a refinement strategy, where optimizing the timing of control switches enhances solution accuracy and system performance. The discussion covers efficient numerical techniques for identifying optimal switching instants and their integration with constrained optimization techniques. For Chebyshev approximation method, it tackles the constrained optimal control problem by iteratively linearizing the dynamics and constraints. The, Chebyshev series expansions are used to approximate both state and control functions of each sub-problem. Crucially, the coefficient functions in the system of the sub-problem are also approximated via Chebyshev series. Utilizing the properties of Chebyshev polynomials, each sub-problem is transformed into an equivalent nonlinear optimization problem subject to linear equality constraints. It can then be solved effectively using any constrained optimization method. Any feasible solution obtained is guaranteed to satisfy the dynamics and constraints exactly over the entire time horizon. We demonstrate the efficacy of both methods through numerical examples. The talk concludes with discussions on future research directions and potential applications.

Bio: Kok Lay Teo received his PhD degree in Electrical Engineering from the University of Ottawa, Canada. He is a John Curtin Distinguished Emeritus Professor at Curtin University, Australia, and a Professor at the School of Mathematical Sciences, Sunway University, Malaysia. He is an Academician of The International Academy for Systems and Cybernetic Sciences (IASCYS), a Fellow of the Asian-Pacific Artificial Intelligent Association (AAIA), a Fellow of The International Artificial Intelligence Industry Alliance (IAIIA), a Fellow of The Australian Mathematical Society (AustMS), and a Senior Member of IEEE (Life). He was the Chair Professor of Applied Mathematics and Head of the Department of Applied Mathematics at the Hong Kong Polytechnic University from December 1998 to December 2004. He then became the Professor of Applied Mathematica and Head of the Department of Mathematics and Statistics from January 2005 to December 2010. He was John Curtin Distinguished Professor at Curtin University from January 2011 until his retirement in November 2019. He is now John Curtin Distinguished Emeritus Professor at Curtin University.He was a member of the Australian Research Council’s (ARC) Mathematical, Information, and Computing Sciences Research Evaluation Committee for the 2010 and 2015 rounds of Excellence in Research for Australia (ERA). Professor Teo has published 6 books and numerous SCI-listed journal papers. His latest book is “Applied and Computational Optimal Control: A Control Parameterization Approach,” by Kok Lay Teo, Bin Li, Changjun Yu, and Volker Rehbock, Springer Optimization and Its Application 171, 2021. He has a software package, MISER3.3, for solving constrained optimal control problems. His current editorial positions include serving as Editor-in-Chief of the Journal of Industrial and Management Optimization and as a member of the editorial board of several journals such as Automatica, Journal of Global Optimization, Journal of Optimization Theory and Applications, Optimization and Engineering, Discrete and Continuous Dynamic Systems, Optimization Letters, and Applied Mathematical Modelling. His research interests include theoretical and computational aspects of optimal control and optimization and their practical applications, such as signal processing in telecommunications, process control, and industrial and management optimization.

Title: AI Enhanced Embodiment Robots for Various Ubiquitous Services

Abstract:  Most of today's robots rely on inherent programs to set up and carry out tasks, but sometimes are having difficulty to use in different kinds of scenarios. Recently the development of AI enabled large-scale models has given robots the ability to be applied to more complex scenes. That is, the robot will rely on the large model to give embodied artificial intelligence, which means that the robot has intelligent behavior and adaptability, and it can interact with the environment and implement actions. It is estimated that the global market size of robots in the intelligent manufacturing automation and many services such as hospital, elder care, hotel, restaurant etc. will reach tens of billions of dollars per year after 2030. It is perceived that embodied intelligent robots consists of components such as controllers, robotic arms, and dexterous hands to achieve perception and interaction with the environment. Enhanced by artificial intelligence, they have the capabilities of semantic understanding, humancomputer interaction, and autonomous decision-making to achieve task understanding and response. The aforementioned issues, challenges and opportunities will be discussed including some research results on intelligent robotics control and manufacturing automation with video demo from our NTU intelligent robotics and automation (iCeiRA) Lab.

Bio: Prof. Ren C. Luo, an IEEE and IET Fellow, received Dipl.-Ing. and Dr.-Ing in EE from the TU Berlin, Germany. He is an Irving T. Ho Chair Professor at National Taiwan University and President of Taiwan R&D Managers Association; He served as Chief Technology Officer of FFG Inc., the world 3rd largest machine tool manufacturer. and was CTO of ASUS corporation, the world major computer manufacturer. He has served two-terms as President and two terms as Dean of Engineering at National Chung Cheng University, one of the major universities in Taiwan, and Founding President of Robotics Society of Taiwan. He was the President of IEEE Industrial Electronics Society.

Prof. Luo was an Assistant Professor, tenured Associate Professor and Full Professor of Dept. of ECE at North Carolina State University, Raleigh, USA. He was Toshiba Chair Professor in the University of Tokyo, Japan. Prof. Luo’s professional expertise includes AI enhanced intelligent robotic control systems, multi-sensor fusion, intelligent manufacturing automation technologies. He has authored over 550 papers, published in refereed international Transactions/ Journals, and conferences.

Prof. Luo has served more than six years as EiC of IEEE Transactions on Industrial Informatics (JIF 12.30) and was more than 5 years as EiC of IEEE/ASME Transactions on Mechatronics. Prof. Luo received IEEE Eugene Mittelmann Outstanding Research Achievement Award, IEEE IROS Innovative Technologies Award; ALCOA Company Outstanding Engineering Research Award, USA. He was General Chair of IEEE ICRA 2003, IEEE IROS 1992, IEEE IROS 2010, IECON 2007.

Title: Adaptive and Learning Model Predictive Control for Autonomous Systems

Abstract. Model predictive control (MPC) is a promising paradigm for high-performance and cost-effective control of complex dynamic systems. This talk will report some recent results on adaptive and learning model predictive control (MPC) for a class of constrained dynamic systems with unknown model parameters. By proactively designing the online estimation mechanism and constructing the tube-based adaptive MPC scheme, the enhanced performance can be achieved compared to the robust tube MPC method. The application of adaptive and learning MPC to unmanned systems will be introduced. Some existing challenges and future research directions will be discussed.

Bio: Yang Shi received his B.Sc. and Ph.D. degrees in mechanical engineering and automatic control from Northwestern Polytechnical University, Xi’an, China, in 1994 and 1998, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, AB, Canada, in 2005. He was a Research Associate in the Department of Automation, Tsinghua University, China, during 1998-2000. From 2005 to 2009, he was an Assistant Professor and Associate Professor in the Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada. In 2009, he joined the University of Victoria, and now he is a Professor in the Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada. His current research interests include networked and distributed systems, model predictive control (MPC), cyber-physical systems (CPS), robotics and mechatronics, navigation and control of autonomous systems (AUV and UAV), and energy system applications.

  On teaching and mentorship, Dr. Shi received the University of Saskatchewan Student Union Teaching Excellence Award in 2007, and the Faculty of Engineering Teaching Excellence Award in 2012 at the University of Victoria (UVic), and the 2023 REACH Award for Excellence in Graduate Student Supervision and Mentorship. On research, he is the recipient of the JSPS Invitation Fellowship (short-term) in 2013, the UVic Craigdarroch Silver Medal for Excellence in Research in 2015, the 2017 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the Humboldt Research Fellowship for Experienced Researchers in 2018; CSME Mechatronics Medal (2023); IEEE Dr.-Ing. Eugene Mittelmann Achievement Award (2023); the 2024 IEEE Canada Outstanding Engineer Award. He is IFAC Council Member; VP on Conference Activities of IEEE IES and the Chair of IEEE IES Technical Committee on Industrial Cyber-Physical Systems. Currently, he is Editor-in-Chief of IEEE Transactions on Industrial Electronics; he also serves as Associate Editor for Automatica, IEEE Transactions on Automatic Control, Annual Review in Controls, etc.

  He is a Fellow of IEEE, ASME, CSME, Engineering Institute of Canada (EIC), Canadian Academy of Engineering (CAE), and a registered Professional Engineer in British Columbia, Canada.

Title: Observer Synthesis for Nonlinear Systems via Physics-Informed Learning

Abstract: A novel learning approach for designing Kazantzis-Kravaris-Luenberger (KKL) observers for autonomous nonlinear systems will be presented. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state with linear dynamics. The observer's state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding such a transformation and its inverse is challenging. We propose to sequentially approximate the maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. The presentation is joint work with Umar Niazi, John Cao, Matthieu Barreau, and Amritam Das.                                

Bio: Karl H. Johansson is Swedish Research Council Distinguished Professor in Electrical Engineering and Computer Science at KTH Royal Institute of Technology in Sweden and Founding Director of Digital Futures. He earned his MSc degree in Electrical Engineering and PhD in Automatic Control from Lund University. He has held visiting positions at UC Berkeley, Caltech, NTU and other institutions. His research interests focus on networked control systems and cyber-physical systems with applications in transportation, energy, and automation networks. For his scientific contributions, he has received numerous best paper awards and various other distinctions from IEEE, IFAC, and other organizations. He has been awarded Distinguished Professor by the Swedish Research Council, Wallenberg Scholar by the Knut and Alice Wallenberg Foundation, Future Research Leader by the Swedish Foundation for Strategic Research. He has also received the triennial IFAC Young Author Prize, IEEE CSS Distinguished Lecturer, IFAC Outstanding Service Award, and IEEE CSS Hendrik W. Bode Lecture Prize. His extensive service to the academic community includes being President of the European Control Association, IEEE CSS Vice President DODA, and Member of IEEE CSS Board of Governors and IFAC Council. He has served on the editorial boards of Automatica, IEEE TAC, IEEE TCNS and many other journals. He has also been a member of the Swedish Scientific Council for Natural Sciences and Engineering Sciences. He is Fellow of both the IEEE and the Royal Swedish Academy of Engineering Sciences.

Title: Towards Automated Learning and Optimization in the Era of Large Models

Abstract. With the rapid advancement of large language models (LLMs), a novel paradigm is emerging in which LLMs not only function as powerful general-purpose learners, but also serve as catalysts for automation across both learning and optimization. This talk offers a systematic overview of our recent research efforts aimed at harnessing the capabilities of LLMs to advance two critical domains: automated machine learning and optimization. In the first part, we explore how LLMs can be leveraged to facilitate various stages of the automated learning process, including algorithm selection, neural architecture search, and the merging of heterogeneous foundation models. Moving beyond conventional approaches that rely heavily on handcrafted or search-based algorithms, LLM-driven methods possess the ability to learn from historical data, uncover structural patterns, and dynamically guide the design of learning pipelines in a more adaptive and scalable manner. In the second part, we turn our attention to the role of LLMs in addressing complex optimization tasks. We demonstrate how multimodal integration empowers LLMs to interpret and solve classical combinatorial problems, assist in the automated discovery and design of optimization algorithms, and adapt pre-trained models for tackling challenging multi-objective optimization scenarios. Finally, we outline a forward-looking roadmap for integrating evolutionary computation with LLMs, identifying key challenges and opportunities at this emerging interdisciplinary intersection.

Bio: Prof. Tan Kay Chen is the Head and Chair Professor of Computational Intelligence in the Department of Data Science and Artificial Intelligence at The Hong Kong Polytechnic University. His research interests encompass evolutionary computation, machine learning, and data analytics. Prof. Tan has co-authored eight books and published over 300 peerreviewed journal articles, which have collectively received more than 34,000 citations on Google Scholar, resulting in an h-index of 93. In recognition of his work, he was named a RGC Senior Research Fellow in 2025 and a Highly Cited Researcher by Clarivate in 2024. For seven consecutive years, he has been listed among the top 2% of the world’s most influential scientists by Stanford University, both for lifetime achievements and annually. Prof. Tan's research has earned numerous accolades, including the 2024 IEEE Computational Intelligence Magazine Outstanding Paper Award, the 2020 IEEE Transactions on Cybernetics Outstanding Paper Award, the 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award, the 2016 IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award, and the 2012 IEEE CIS Outstanding Early Career Award. He has also held professional positions in various academic organizations related to computational intelligence, including Vice President of the IEEE Computational Intelligence Society (2021-2024) and Co-Editor of the Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications. From 2015 to 2020, he served as the Editor-in-Chief of IEEE Transactions on Evolutionary Computation and from 2010 to 2013 for IEEE Computational Intelligence Magazine. In 2014, he was elevated to IEEE Fellow in recognition of his contributions to evolutionary computation.