Title :  Spatial AI and Autonomous Robot Navigation Technology

Abstract:

This lecture introduces physical AI and spatial AI technologies for autonomous robot navigation. In addition to technical introductions to core technologies such as SLAM (Simultaneous Localization And Mapping), path planning, and motion control, autonomous navigation frameworks and application cases for operating mobile robots, drones, and legged robots in various environments such as rough terrain are introduced, and the future prospects for spatial AI technology are discussed.

Title :  Applications of Complex Communication Sciences in Next Generation Wireless Communication Systems

Abstract:

The Technical Committee on Complex Communication Sciences (CCS), IEICE, was established in 2011, and has collaborated with the Korea Multimedia Society through the organization of international workshops.  In this talk, I will present our research activities in collaborations within CCS or KJCCS/JKCCS with a focus on their applications to wireless communication systems. These include studies on synchronization phenomena in nonlinear oscillators, multi-armed bandit algorithms and applications of photonic computing,  pulse-based communication protocols inspired by brain dynamics, and research on quantum computing  conducted through international collaboration.

Title :  Advances in Cybersecurity Modeling and Analysis: Methods, Applications, and Lessons Learned

Abstract:

Graphical models for security combine graph theory and probabilistic reasoning to visually represent attack paths and analyze system vulnerabilities and defenses. Recent advances include extended forms like 2-level and N-level HARMs that enhance scalability and expressiveness. This talk covers key developments in these modeling techniques and explores their applications in areas such as automated red team/blue team exercises, IoT, cloud environments, and cyber-physical systems. Through diverse examples, we highlight the strengths and challenges of using graphical models for cybersecurity analysis and decision-making in complex, evolving threat landscapes.

Title :  The Present and Future of Explainable AI (XAI): Toward Trustworthy Artificial Intelligence 

Abstract:

This talk explores the foundations, techniques, and applications of Explainable AI (XAI), highlighting its importance in high-stakes domains like healthcare, finance, autonomous driving, and law. It contrasts transparent model designs with post-hoc explanation methods such as Grad-CAM, LIME, SHAP, and LRP, examining their strengths in different contexts. A key focus is on the growing role of Large Language Models (LLMs) in improving AI explainability by generating human-readable narratives from complex model outputs. This talk concludes by discussing strategies to evaluate explanation effectiveness and emphasizes that XAI is essential not only for technical transparency but also for building responsible and trustworthy AI systems.