Hyunwoong Ko
Assistant Professor, School of Manufacturing Systems and Networks
Dr. Hyunwoong Ko is an Assistant Professor in the School of Manufacturing Systems and Networks at the Ira A. Fulton Schools of Engineering, Arizona State University. He earned his Ph.D. in the School of Mechanical and Aerospace Engineering at Nanyang Technological University (NTU) in September 2019. During his Ph.D. studies and subsequent postdoctoral training, he worked at the National Institute of Standards and Technology (NIST) as a research associate until September 2021.
Dr. Ko’s research focuses on data science, manufacturing science, and design science, with particular emphasis on their intersections. His work aims to establish foundational principles for Physical Artificial Intelligence (AI) and digitalization in manufacturing and design. These foundations enable tighter integration of AI and machine learning, cyber-physical systems, and digital twins across multiple spatial and temporal scales, particularly in advanced manufacturing domains such as semiconductor manufacturing, additive manufacturing, and robotics-based manufacturing.
Ultimately, his research seeks to enhance control and decision-making in manufacturing and design—spanning areas such as design for manufacturing, in-situ monitoring and control, and ex-situ evaluation—by leveraging AI-driven insights derived from emerging data and knowledge generated through both virtual and physical systems.
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My research focuses on the intersection of data science, manufacturing science, and design, with an emphasis on enabling physical AI–driven digital transformation in advanced manufacturing systems. I develop physics-informed and generative machine learning methods, including digital twins and diffusion models, to integrate cyber-physical systems across multiple spatial and temporal scales. My work is particularly applied to complex manufacturing domains such as semiconductor processes and robotics-enabled production. Ultimately, my research aims to enhance predictive modeling, control, and decision-making in manufacturing and design, including applications in virtual metrology, in-situ monitoring, model predictive control, and design for additive manufacturing.