Title:Industrial AI: Opportunities, Applications, and Challenges
Abstract
Industrial AI is emerging as a transformative field at the intersection of artificial intelligence and large-scale engineering systems. This talk provides a high-level perspective on how modern AI methods are applied in real industrial environments to enhance reliability, augment human decision-making, and improve operational efficiency, while highlighting the unique constraints of safety-critical settings and the persistent gap between research prototypes and deployment. It illustrates these themes through practical applications in predictive maintenance, knowledge extraction, and operational optimization, including guided visual inspection systems that assist technicians in real time, reinforcement-learning–based dynamic dispatching for warehouse and logistics operations, and automated fault-tree construction for accelerated root-cause analysis. Together, these examples demonstrate how Industrial AI can translate advanced methods into deployable systems that meaningfully augment expertise and performance, while pointing to open challenges and opportunities for impactful collaboration between academia and industry.
Speaker Bio
Dr. Xian Yeow Lee is a Senior Research Scientist in the Industrial AI Lab at Hitachi America, Ltd., focusing on deep learning, generative AI, foundation models, and reinforcement learning for industrial applications, including visual inspection, robotics, and supply chain optimization. His research centers on developing robust and scalable learning algorithms that integrate AI with complex engineering and manufacturing systems, bridging AI theory with practical solutions across Hitachi’s key sectors: Digital Systems & Services, Green Energy & Mobility, and Connective Industries. Previously, he worked on power system control, additive manufacturing, and inverse design during research appointments at Siemens and Lawrence Livermore National Laboratory.
