PROGRAMME

This session aims to have top technical experts in the ICT field present and engage in discussions to promote international technology cooperation between Korea and Europe. Experts from each field will introduce their respective institutions and present areas of interest for Korea–EU international joint R&D. Additionally, the networking established through this session will be leveraged to create opportunities for future EU projects and Korean international joint R&D programs, as well as consortium formation.

In the rapidly evolving technological landscape, the traditional boundaries between hardware and intelligence are being rewritten. The session, "Bridging Semiconductor Innovation and AI-Driven Sensor Systems," explores the critical intersection where advanced semiconductor design meets AI-driven perception. For decades, sensor systems functioned as the passive "nervous system" of our digital infrastructure, merely capturing raw signals to be sent elsewhere for analysis. However, as the demands for real-time processing in autonomous vehicles and medical diagnostics increase, this centralized model has reached its limit. We are now entering an era where the sensor itself must be architected to support intelligent decision-making, powered by innovations in high-precision analog design and advanced CMOS image sensing. The primary objective of this session is to dissect how semiconductor innovation is moving beyond simple scaling to embrace specialized hardware capable of delivering high-fidelity data for AI algorithms. Instead of focusing on generic processing units, we will explore how the "intelligence" of a system begins at the analog front-end. By optimizing signal-to-noise ratios (SNR), enhancing dynamic range, and implementing low-latency data paths, we create the essential foundation that allows AI to function reliably in complex environments. This bridge is critical because the quality of AI-driven insights is fundamentally limited by the precision of the hardware that captures the original signal. Throughout this session, we will examine the technical pillars that make this bridge possible, drawing on real-world advancements in automotive and medical sectors. We will delve into High-Dynamic-Range (HDR) and LED Flicker Mitigation (LFM) technologies in CMOS Image Sensors, which are vital for the "vision" of AI-driven autonomous systems. Furthermore, we will discuss the role of Heterogeneous Integration, allowing the stacking of sensing layers with advanced logic to reduce power consumption and footprint—a necessity for wearable diagnostics and in-cabin monitoring. We will also touch upon Low-Power Analog Front-End (AFE) designs that enable "always-on" monitoring without compromising the energy efficiency of the system. Ultimately, this discussion focuses on the birth of a more perceptive and responsive digital reality. As we look toward the future, the synergy between advanced semiconductors and AI-driven sensors will redefine industries—from smart factories to self-driving cars. This session provides a comprehensive roadmap for system architects, IC designers, and AI researchers who seek to understand how the next generation of semiconductors will serve as the physical substrate for a truly aware world. By bridging these two fields, we are building systems that do not just collect data, but provide the immediate, actionable insights required for the next decade of autonomy.

As the global telecommunications ecosystem transitions from conceptual frameworks to technical specifications, the road to 6G is being defined by a fundamental shift: the convergence of connectivity, perception, and intelligence. Building on last year’s foundational discussions in Vienna, this session dives deeper into the two transformative pillars of the next generation: Integrated Sensing and Communication (ISAC) and AI-Native Mobile Networks. While 6G continues to encompass a broad spectrum of innovations including Non-Terrestrial Networks (NTN), Quantum Security, and Sub-THz communications, this year’s session prioritizes the synergy between the physical and digital worlds. We will explore how ISAC enables the network to act as a sensor, providing environmental awareness for autonomous systems and smart cities, and how AI/ML is being woven into the very fabric of the air interface and network management to create truly self-optimizing architectures. Key areas of exploration will include: ● The ISAC Paradigm: Technical challenges in waveform design, interference management, and the hardware evolution required to unify sensing and data transmission. ● AI-Native Connectivity: Moving beyond "AI-assisted" to "AI-defined" networks, focusing on energy-efficient edge intelligence and predictive resource allocation. ● Standardization & Global Alignment: Updates on the ITU-R IMT-2030 framework and the roadmap toward 3GPP Release 21 and beyond. ● Sustainability and Trust: Achieving "Green 6G" through intelligent power saving and ensuring the security of decentralized, AI-driven business models. This session serves as a high-level collaborative forum for researchers, engineers, and industry leaders to bridge the gap between theoretical research and practical implementation.

Recent advances in semiconductor technologies—including wide bandgap materials such as SiC and GaN, emerging device and packaging materials such as diamond, and progress in optoelectronics—are redefining the capabilities of electronic and communication systems. Combined with developments in next-generation wired and wireless networks, these technologies are enabling new approaches to sensing, energy delivery, and data-intensive applications. Artificial intelligence is increasingly central to this progress, accelerating design, optimising performance, and enabling adaptive, data-driven operation of complex electromagnetic systems. RF and microwave technologies provide a versatile platform for interdisciplinary research, extending well beyond traditional communications. They are now being applied to non-invasive physiological monitoring, rapid diagnostics of infectious diseases, advanced material characterisation, and intelligent sensing in complex environments. AI-driven modelling, signal processing, and inference are key to enhancing sensitivity, robustness, and interpretability in these applications. This session brings together researchers from fundamental sciences, engineering, and industry working at the intersection of RF and microwave technology and AI-enabled methodologies. Emphasis will be placed on advanced microwave sensing systems and the role of high-power and high-efficiency amplifiers as enabling components. These technologies are critical for extending sensing range, improving signal quality, and supporting high-energy applications in biomedical research, materials science, and future communication infrastructures. The session aims to foster Europe–Korea collaboration and stimulate innovation toward an AI-driven future of science and technology.

The rapid evolution of personalized healthcare demands implantable medical devices (IMDs) and sensors that are smaller, smarter, and more biocompatible. This session focuses on the critical role of Heterogeneous Integration (HI) in bridging the gap between advanced functional materials and clinical application. We will explore cutting-edge packaging technologies, including System-in-Package (SiP), 3D stacking, and flexible/stretchable interconnects, which enable the seamless integration of MEMS, CMOS, and power sources into ultra-compact form factors. Key discussion topics include: Biocompatible Encapsulation: Innovative wafer-level packaging and thin-film coatings to ensure long-term reliability in harsh physiological environments. Miniaturization & Power Efficiency: Strategies for integrating high-density energy storage and wireless power transfer modules. Sensor Fusion: Techniques for co-packaging multi-modal sensors (chemical, electrical, and physical) with low-power signal processing units. This session aims to foster collaboration between packaging engineers and medical device designers to address the manufacturing challenges and regulatory standards of the next decade's bio-electronic systems.

The rapid advancement of deep learning and large-scale foundation models has transformed virtually every branch of science and technology. Yet, as AI systems are deployed in high-stakes domains (from healthcare and scientific discovery to engineering design and public policy) critical limitations of purely data-driven approaches have become increasingly apparent. Neural networks, despite their remarkable pattern recognition capabilities, often function as opaque black boxes that struggle to incorporate domain knowledge, guarantee logical consistency, or provide human-understandable explanations. These shortcomings are especially problematic in settings that demand regulatory compliance, safety assurance, and stakeholder trust. Neuro-Symbolic AI (NeSy AI) addresses these challenges by integrating the learning power of neural networks with the reasoning capabilities of symbolic methods. Rather than treating learning and reasoning as competing paradigms, NeSy AI combines them synergistically: neural components extract patterns and representations from raw data, while symbolic components, such as knowledge graphs, ontologies, logical rules, and formal models, enforce structure, enable interpretable inference, and incorporate expert knowledge. This hybrid paradigm has shown considerable promise across a wide spectrum of applications, including drug discovery, autonomous systems, natural language understanding, robotics, and industrial process optimization. Recent breakthroughs have further accelerated the convergence of neural and symbolic methods. Large language models (LLMs) have demonstrated emergent reasoning abilities, prompting new research into how structured knowledge can enhance their reliability and factual grounding. Simultaneously, advances in graph neural networks, probabilistic programming, and differentiable reasoning have opened new avenues for seamlessly integrating symbolic structures into end-to-end learning pipelines. These developments position NeSy AI as a key enabler of the next generation of AI systems that are not only performant but also transparent, fair, and accountable. In line with the EKC2026 theme, "AI-Driven Future of Science and Technology," this session aims to bring together researchers and practitioners from diverse scientific and engineering backgrounds to explore the foundations, methods, and applications of Neuro-Symbolic AI. The session will cover a broad range of topics, including but not limited to: • Foundations of neuro-symbolic integration: architectures, learning paradigms, and theoretical frameworks • Knowledge-enhanced neural models: combining LLMs, knowledge graphs, and domain ontologies • Explainability, fairness, and trustworthiness in hybrid AI systems • Applications in scientific discovery, healthcare, manufacturing, and engineering • Neuro-symbolic methods for process intelligence, decision support, and autonomous systems • Bridging data-driven learning and model-based reasoning in real-world systems By fostering dialogue between the European and Korean AI research communities, this session seeks to identify emerging research directions, share best practices across disciplines, and catalyze collaborations that advance both the theoretical foundations and practical impact of Neuro-Symbolic AI. We welcome contributions from all areas of science and technology where the integration of learning and reasoning can drive innovation toward a more intelligent, transparent, and responsible future.

Background and Motivation The convergence of artificial intelligence and the physical world is redefining the frontiers of science and technology. Physical AI—encompassing computer vision, robotic perception and manipulation, autonomous systems, and human-robot interaction—is rapidly transitioning from theoretical research to transformative real-world applications. From intelligent construction monitoring to autonomous laboratory platforms, and from surgical robotics to AI-driven pharmaceutical discovery, Physical AI is becoming a foundational technology that cuts across engineering, life sciences, and industry. Purpose and Significance This session aims to bring together leading researchers and practitioners at the intersection of AI and physical systems, showcasing how cutting-edge machine learning, computer vision, and robotic technologies are being deployed to solve complex, real-world challenges. Aligned with the EKC 2026 theme of 'AI-Driven Future of Science and Technology', the session will explore the following key thematic areas: • Foundations of Physical AI: Core methodologies for enabling machines to perceive, understand, and interact with the physical world—including 6D pose estimation, gaze and motion prediction, hand-object interaction modelling, and embodied AI. • Digital Twins and Smart Infrastructure: The application of AI-powered digital twins and robotic monitoring systems in civil engineering, structural health monitoring, and built environment management. • Autonomous Robotics and Robot Learning: Recent advances in robot learning for manipulation and locomotion, language-conditioned robot control, and reinforcement learning for autonomous platforms. • AI in Manufacturing and Autonomous Laboratories: How Physical AI enables intelligent automation on the factory floor, including quality control, process optimisation, and fully autonomous 'self-driving' laboratory systems for accelerated materials and drug discovery. • AI Applications in Biomedicine and Pharmaceuticals: Robotics-assisted surgery, AIguided drug screening, automated bioassay platforms, and intelligent sensing for healthcare and life science research. By convening experts from top European and Korean universities, as well as industry partners, this session will foster meaningful interdisciplinary dialogue and identify strategic opportunities for UK-Korea and Europe-Korea research collaboration. The session is designed to be highly interactive, featuring invited talks, panel discussion, and a call for contributed presentations from the wider EKC community.

This session is part of the “Advanced Mobility in the AI Era” series of the Advanced Mobility Expert Network (AMExNet), which approaches advanced mobility from interdisciplinary perspectives. In this series, the MA division focuses on carriage systems, while the BEED division addresses policy and social environments. Complementing these perspectives, the EI session will explore the broader societal landscape that shapes urban mobility patterns, as well as the technological dimensions of mobility infrastructure in the age of Artificial Intelligence. AI technologies are reshaping everyday life, urban systems, and societal structures at an unprecedented pace. While convenience, efficiency, and economic incentives have been key drivers of AI development and deployment, we must move beyond merely adapting to technological change. AI must be leveraged proactively and strategically to advance broader societal objectives, including collective well-being, environmental sustainability, and equitable access. This session explores how AI-driven technological innovations are transforming lifestyles, transportation-related infrastructure systems, and their operations. Key areas of focus include mobility-related data governance, infrastructure design, and system operations. The session will also address how AI can be integrated into public infrastructure to strengthen inclusiveness, resilience, and democratic oversight. The session will begin with two invited talks, followed by a moderated roundtable discussion addressing key questions, including: •AI-driven technologies are bringing about profound transformations across the full spectrum of industries and public sectors such as transportation, energy infrastructure, education, public administration, etc. In this context, how can technological innovation be strategically steered to enhance accountability, foster equity, and advance the broader public good? More precisely, in what ways can AI-enabled infrastructure systems be designed to ensure inclusivity and equitable access? •What role can (AI-driven) technological innovation play in policy development and policy-making to better serve the public interest? •How can we ensure that data serves the public interest in a context of excessive concentration of control over data within the private sector? In what ways can technology and/or policy be leveraged to support this goal?

Computer science is a discipline defined by its commitment to precise reasoning about computational behaviour. From its origins in recursion theory and mathematics, the subject has treated programs not merely as executable algorithms, but as objects whose meaning and correctness can be analysed and explained. Reasoning about programs is therefore not an advanced or optional concern, but rather it is foundational to what computer science actually is. Despite this history, introductory computer science education has marginalised reasoning in favour of program construction. Students are taught to write code early and often, while the activities that distinguish computer science from ad hoc software production, such as specification, invariants, correctness arguments, and principled debugging, are delayed or omitted altogether. Debugging, in particular, is frequently framed as a reactive skill, used only after failure, rather than as a central mode of inquiry into program construction and behaviour. This session is about advancing a strong claim: the separation of programming from reasoning and debugging represents a fundamental misalignment between computer science education and the intellectual core of the discipline. With the rapid advancement of AI and there are new ways we can correct this misalignment which we believe is no longer optional. It is now a structural necessity for the education of competent computer scientists and this session aims to bring together ideas on how we can teach computing in this new age.

The rapid advancement of artificial intelligence (AI), machine learning, and large-scale data infrastructures is fundamentally reshaping the way science and technology engage with society. Social systems - ranging from urban mobility and public health to climate adaptation, misinformation, inequality, and governance - are increasingly mediated by digital platforms and data-generating technologies. At the same time, these systems produce vast streams of behavioral, economic, environmental, and network data. This convergence presents a transformative opportunity: to integrate computational methods with social science theory in order to better understand, predict, and address complex societal challenges. This session, Data Analytics for Societal Challenges: Bridging Computational and Social Sciences, aims to foster interdisciplinary dialogue at the intersection of AI, data analytics, and social inquiry. Computational social science has emerged as a powerful field that combines statistical modeling, network analysis, natural language processing, simulation, and AI-driven approaches with substantive knowledge from sociology, economics, political science, psychology, and related disciplines. However, meaningful progress requires deeper integration between methodological innovation and contextual understanding of social phenomena. Aligned with the EKC2026 theme, AI-Driven Future of Science and Technology, this session highlights how AI is not only a technological advancement but also a scientific paradigm shift. AI enables the analysis of high-dimensional, dynamic, and heterogeneous data that were previously inaccessible, opening new frontiers in modeling social complexity. From predictive models for public policy to agent-based simulations of collective behavior and AI-assisted decision systems, data analytics is transforming how societal problems are conceptualized and addressed. The purpose of this session is threefold: 1. Advance methodological innovation by showcasing cutting-edge AI and data-driven techniques applied to societal challenges. 2. Strengthen interdisciplinary collaboration by connecting computational researchers with social scientists and policy-oriented scholars. 3. Promote responsible and ethical AI applications that consider bias, fairness, transparency, and societal impact. The significance of this session lies in its forward-looking perspective. As AI systems increasingly shape economic systems, information ecosystems, and governance structures, understanding their societal implications becomes essential for sustainable and inclusive technological development. By bridging computational and social sciences, this session contributes to shaping an AI-driven future that is not only technologically advanced but also socially informed, ethically grounded, and responsive to real-world challenges. Ultimately, the session aims to position data analytics as a central pillar in the future of science and technology - one that enables deeper insight into human behavior, supports evidence-based policy, and strengthens the societal relevance of AI innovation.