Confirmed Plenary Keynote Speakers

Prof Jay Lee is the Clark Distinguished Chair Professor and Director of the Industrial AI Center at the University of Maryland, College Park, USA. An internationally recognized pioneer in Industrial AI and smart manufacturing, Prof Lee has spent over three decades transforming industrial operations from “fail-and-fix” to “predict-and-prevent” paradigms.

From 2019 to 2021, Prof Lee served as Vice Chairman and Board Member of Foxconn Technology Group, where he led the digital transformation of the world’s largest contract manufacturer. During this tenure, he spearheaded the development of several World Economic Forum (WEF) Lighthouse Factories, demonstrating the scalable impact of Industrial AI in high-volume production.

His current research focuses on next-generation machine learning—including Stream-of-X AI, Industrial Large Knowledge Models (ILKM), and Physical AI—a field he recently advanced through the World Economic Forum’s 2025 report on resilient operations. He leads the Data Foundry, a premier repository of over 100 industrial datasets, and the AI Factory initiative, an open laboratory designed to cultivate industry-ready engineering talent.

Prof Lee’s global leadership includes serving on the World Economic Forum’s Global Future Council on Advanced Manufacturing, the Board of Governors of the Manufacturing Leadership Council (NAM), and as a Senior Advisor to McKinsey. He is the Editor-in-Chief of the IOP Journal Machine Learning: Engineering and a Fellow of ASME, SME, ISEAM, and the PHM Society. His numerous honours include the SME Eli Whitney Productivity Award (2022) and the SME/NAMRC S.M. Wu Research Implementation Award (2022). His 2020 book, Industrial AI: Applications with Sustainable Performance, is widely considered the seminal text defining the field as a systematic engineering discipline.

Prof Dr Dimitris Kyritsis (Kiritsis) is Professor Emeritus of ICT for Sustainable Manufacturing at EPFL, Lausanne, Switzerland, Academic Director of the EPFL Extension School program on Sustainable Resilient Value Chains, Senior Advisor at the University of Oslo, Norway, and Senior Advisor at the Swiss Smart Factory, Biel/Bienne, Switzerland. His research interests are Closed Loop Lifecycle Management, Industrial Ontologies, Knowledge Graphs and Cognitive Digital Twins. Dimitris is involved in EU projects in the areas of Digital Manufacturing, Zero Defect Manufacturing, Circular Manufacturing, etc. He has more than 250 publications. From 2013 to 2019 Dimitris was the Chair of IFIP WG5.7-Advances in Production Management Systems. He is co-founder of the Industrial Ontologies Foundry and of the Knowledge Graph Alliance. Dimitris was Member of the WEF Global Future Council on Advanced Manufacturing and Value Chains from 2019 to 2022. Since 2022 he is Editor-in-Chief of the Open Access journal Frontiers in Manufacturing Technology.

Abstract: An innovative strategy created to tackle the difficulties of the digital and green transition in industrial sectors is the Information Modelling Framework (IMF): https://www.sciencedirect.com/science/article/pii/S2950550X25000081 and https://www.dnv.com/digital-trust/recommended-practices/asset-information-modelling-dnv-rp-0670/. In order to achieve sustainability and resilience targets and accommodate digital transformation and new business models, IMF seeks to revolutionize the planning, building, and operation of physical industrial assets. Effective use of AI and Digital Twin technologies in industrial applications is made possible by the IMF approach, which offers engineers easily navigable information modelling tools. IMF’s key features include encapsulating model-based systems engineering principles into ontology-based classification systems, transforming system models into ontologies and knowledge graphs that capture entity meanings across design phases, and resolving information fragmentation issues in a variety of industries. The IMF methodology helps the industrial sector overcome the twin problems of the digital and green transition by facilitating dependable data integration, enhancing decision-making, and promoting the digitization of industrial processes. In this talk, IMF will be presented with use case examples from the Digital Europe project SM4RTENANCE: https://sm4rtenance.eu/.

Prof I-Ming Chen received BS degree from National Taiwan University in 1986, and MS and PhD degrees from California Institute of Technology, Pasadena, CA in 1989 and 1994 respectively. He is currently Full Professor in the School of Mechanical and Aerospace Engineering, Director of Bachelor’s Degree Program in Robotics, and Co-Director of CARTIN (Center for Advanced Robotics Technology and Innovation) in Nanyang Technological University (NTU), Technical Advisor to National Robotics Program Office in Singapore, and Certified Patent Valuation Analyst (CPVA). He was Editor-in-chief of IEEE/ASME Transactions on Mechatronics from 2020 to 2022, and Director of Robotics Research Centre (NTU) from 2013 to 2017. Professor Chen is Fellow of Singapore Academy of Engineering (SAEng), Fellow of IEEE and Fellow of ASME, General Chairman of 2017 IEEE International Conference on Robotics and Automation (ICRA 2017) in Singapore. His research interests are in Robot AI & perception, reconfigurable industrial AI, logistics and construction robots and human-robot interaction. He founded and managed Transforma Robotics Pte Ltd – a pioneer in construction robotics and Hand Plus Robotics Pte Ltd – a logistics robot and AI company. He has mentored many robotics-related startups and entrepreneurial teams in Singapore and China.

Abstract: Global industries face critical pressures, including acute manpower shortages and aging populations, necessitating a transition from manual robotic programming to a holistic system perspective. This keynote explores the integration of robotics and AI through a hierarchical structure, moving from top-level business and supply chain planning down to autonomous, machine-level execution. Central to this framework is Physical AI, which differs from digital-only models by requiring a sophisticated, inseparable bond between hardware and software. Successful real-world deployment relies on “Domain Data”–the specialized expertise and field experience accumulated within specific industries–to ensure robustness in non-standardized environments. We will discuss practical applications in sectors such as food handling, intelligent logistics, hospital supply and cleaning automation, where AI-driven 3D perception and grasp planning enable robots to navigate high-mix tasks previously reserved for human workers. These systems represent a “Hybrid Automation” approach, combining AI’s high-level decision-making with deterministic, safety-critical motion control.

Looking forward, the next frontier in Embodied AI involves pushing intelligence down to the most granular level. Our future work conceptualizes Embedded AI for robotic joint actuators, introducing a “neuromuscular-inspired” reflex layer. By embedding fast-real time intelligence directly into the robot’s “muscles,” we aim to create actuators that are self-aware and proactive, capable of responding to electrical or mechanical shocks and adapting to their own fatigue states independent of a central controller. Drawing on 30 years of R&D and entrepreneurial experience, I will outline how this systematic integration allows robots to complement human skills. While the journey toward AGI and widely deployed humanoids continues, our immediate focus remains on building a robust, hierarchical intelligence framework to ensure industrial systems are reliable, cost-effective, and sustainable.

Prof Seunghwa Ryu is an Endowed Chair Professor of Mechanical Engineering and Head of the Department of AX (AI+X) at KAIST. He also serves as Director of the KAIST InnoCORE PRISM-AI Center, a national research initiative dedicated to AI-driven intelligent design and manufacturing integration. He received his PhD in Physics from Stanford University (2011) and his BS in Physics from KAIST (2004). He has held visiting professorships at the University of California, Berkeley (2024) and Stanford University (2018). Prof Ryu’s research focuses on developing knowledge-guided and physics-informed AI frameworks that enable data-efficient inverse design, multiscale material modeling, and intelligent process optimization. His work bridges fundamental mechanics, machine learning, and industrial deployment. He has published over 180 papers in leading international journals, including Science, Nature Communications, Advanced Materials, npj Computational Materials, and Computer Methods in Applied Mechanics and Engineering. He is a recipient of the APACM Award for Young Investigators in Computational Mechanics (2019) and was elected to the Young Korean Academy of Science and Technology (Y-KAST) in 2022.

Prof Yasushi Umeda is a Full Professor at Department of Precision Engineering, School of Engineering, the University of Tokyo, Japan. He holds BE, ME, and Dr Eng in Precision Machinery Engineering from the University of Tokyo. He authored/edited 29 books, over 150 peer-reviewed articles, and has 19 patents granted/pending. Five of his papers won best paper awards in scientific journals and international conferences. He is a Fellow of CIRP (International Academy for Production Engineering), a Fellow of JSME (Japan Society of Mechanical Engineers), a director of JSPE (Japan Society for Precision Engineering), and a member of ASME (American Society of Mechanical Engineers). His research interests include smart manufacturing systems, circular economy, life cycle engineering, eco-design, maintenance, and design theory.

Zhang “Mercy” Huize graduated from the University of Illinois Urbana–Champaign. At SUPCON, he leads the International R&D Center and founded FREEZONEX. He is dedicated to the research and practice of software-defined control systems, AI-driven process optimization, and industrial data integration technologies.

Abstract: Every chemical and energy company needs a large amount of closed-loop process optimization software to improve throughput and product quality while reducing human error. For decades, this field has been dominated by first-principles, control-theory-based approaches. Today, combining AI with first-principles models has become a new best practice. In this talk, I will introduce how to integrate reinforcement learning with MPC to achieve performance beyond pure first-principles methods in real plants and complex control scenarios.

Dr Leon Chan is the Chief Executive Officer of Alpha X Technology where he leads the company’s global vision to become a trusted partner for intelligent, sustainable and AI-driven transformation. With more than 20 years of leadership experience across artificial intelligence, healthcare, digital transformation and high-tech manufacturing, Leon is widely recognized for bridging advanced technologies with real-world operational impact.

Prior to joining Alpha X, Leon held executive and strategic leadership roles at Siemens Healthineers, Fosun Group, Philips, and McKinsey, where he drove large-scale innovation initiatives, digital strategy execution and enterprise transformation programs across international markets. His work consistently focuses on translating complex technologies into measurable business outcomes, including productivity improvement, quality optimization and sustainable growth.

At Alpha X, Leon oversees end-to-end AI transformation initiatives that integrate data infrastructure, intelligent automation and decision intelligence across industries. He is a strong advocate of Industry 4.0 adoption, championing the convergence of hardware, software and AI to enable organizations to operate with transparency, traceability, and trust in an increasingly data-driven world.

Leon holds a PhD in Biomedical Informatics from Columbia University. His multidisciplinary academic foundation complements his executive leadership, allowing him to align scientific rigor with commercial strategy and innovation execution.

Forest leads McKinsey’s Manufacturing & Supply Chain and Consumer Operations practices in Greater China. He joined McKinsey & Company in 2011, with a core focus on end-to-end AI transformation—including strategy development, diagnostic assessment, solution design/deployment, and full-scale implementation—as well as IoT ecosystem development and digital talent capability building.

Before joining McKinsey, Forest accumulated 17 years of senior operations leadership experience across the automotive, semiconductor, machining industries. During 15+ years at McKinsey, he has worked with, served, and collaborated with over 500 enterprises worldwide, spanning multinational corporations, state-owned enterprises, and private-sector companies of varying scales and industries. His client portfolio covers key sectors including automotive, high-tech, new energy, machinery, and basic materials.

A recognized expert appointed by the World Economic Forum (WEF), Forest has helped more than 80 global companies in achieving Global Lighthouse status. He is also the founder and leader of the Tsinghua-McKinsey Digital Capability Center, and serves as a Guest Professor at both Tsinghua University and Shanghai Jiao Tong University.

Abstract: Humanoid robots will first realize scaled application in manufacturing, thanks to structured factory environments and clear labor substitution value. They integrate capacities of industrial, collaborative and mobile robots to conduct assembly, sorting, inspection and equipment maintenance. Nevertheless, large-scale rollout is hindered by four core hurdles: human-machine co-working safety, insufficient battery endurance, poor cross-scenario generalization of world models, and prohibitive hardware costs. Technical upgrades on dexterous hands, bipedal mobility and embodied AI will cut costs and unlock huge industrial productivity opportunities.

Prof Yongfeng Lu is the Lott Distinguished University Professor in the Department of Electrical and Computer Engineering at the University of Nebraska-Lincoln (UNL) since September 2002, with a courtesy appointment in Mechanical and Materials Engineering since 2018. A globally recognized leader in laser-based micro/nanoscale material processing, his research focuses on femtosecond laser nanofabrication, laser resonant vibrational excitation, nanophotonics, optical spectroscopy, and advanced material synthesis (diamond films, graphene, 2D materials) for clean energy, fusion targets, sensing, and manufacturing. He pioneered resonant laser-assisted CVD growth of high-quality diamond and GaN, laser desensitization/peening for U.S. Navy aluminum alloys, two-photon polymerization for inertial confinement fusion fuel targets, and cryo-CARS microscopy for fusion diagnostics. With 604 journal articles, 524 conference papers, 28 patents, over 31,000 citations (h-index 86), and more than $45 million in funding, he ranks among the world’s top 2% of scientists. Honored with the 2024 SPIE 3D Printing Award and LIA Schawlow Award, and a Fellow of SPIE, LIA, OSA, and IAPLE, Prof. Lu has mentored 38 PhD graduates and 53 postdocs while serving as Chairperson of IAPLE, past President of LIA, and Editor-in-Chief of the Journal of Laser Applications.

Abstract: Three-dimensional nanofabrication via two-photon polymerization (TPP) offers a unique and highly versatile capability for creating complex structures. It enables the flexible design and fabrication of intricate features across one to three dimensions, spanning length scales from micrometers to centimeters, while achieving spatial resolutions finer than 100 nm. Over the past 15 years, the Laser-Assisted Nano Engineering Group at the University of Nebraska-Lincoln (UNL) has collaborated with the Laboratory for Laser Energetics (LLE) at the University of Rochester. We focused on developing practical, reliable TPP-based approaches for fabricating a wide variety of high-precision target structures required for inertial confinement fusion (ICF) experiments. At the same time, thorough inspection and quality control of ICF fuel capsules remain critical to experimental success. These capsules are commonly fabricated from plastic materials such as polystyrene (PS). However, the manufacturing process frequently introduces embedded defects in the form of small voids known as vacuoles within the shell walls. These vacuoles, typically ranging in size from 100 to 2000 nm, can distort the implosion symmetry, disrupt uniform compression, and significantly degrade the overall performance and energy yield of the capsules during ICF experiments. To address this challenge, coherent anti-Stokes Raman scattering (CARS) microscopy has proven to be a powerful, and non-destructive imaging technique capable of detecting, characterizing, and mapping both surface and internally embedded defects with excellent spatial resolution and chemical specificity.