Biography: 

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 additive manufacturing, semiconductor 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. 

CV: Curriculum Vitae

Ph.D., Mechanical and Aerospace Engineering (Sep. 2019)

  • Nanyang Technological University (NTU)
  • Agency for Science Technology and Research (A*STAR) scholar
  • Research Associate at the National Institute of Standards and Technology (NIST)

M.S., Industrial and Management Engineering (Feb. 2012)

  • Hanyang University

B.S., Industrial Engineering (Feb. 2010)

  • Hanyang University (ERICA)
  • 1st ranked in the graduating class

Research Interests: 

Professional Experience:

Honors & Awards (Selected):

Invited Talks (Selected):

  1. Ko, H., “Toward Advanced Physical AI and Digital Twins for Manufacturing and Design: Generative, Graph, and Attention-based Learning,” SIE Seminar Series, University of Arizona, Tucson, United States, Nov. 20, 2025
  2. Ko, H., “Generative Modeling for Predictive Manufacturing Digital Twins” Workshop: Data Management and Digital Twins for Advanced Manufacturing, ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2025), Anaheim, California, USA, Aug. 17, 2025
  3. Ko, H., “AM Transformer: A Koopman Theory-Based Transformer to Learn Additive Manufacturing Dynamics,” Webinar: Advanced Data Analysis and Knowledge Models in Smart Manufacturing, International Journal of AI for Materials and Design, Nov. 11, 2024.
  4. Ko, H., “Generative and Transformer Modeling Approaches for Advanced Manufacturing,” Keynote Speaker, LG 2024 Laser Technology Forum, LG Electronics, South Korea, Sept. 26, 2024.
  5. Ko, H., “Generative Diffusion Modeling for Predictive Digital Twins of Sustainable Nanoparticle Electronics Printing,” The 18th U.S.-Korea Forum on Nanotechnology, Tempe, Arizona, Sept. 23, 2024.
  6. Ko, H., “Diffusion and Transformer Modeling for Predictive Digital Twins in Advanced Manufacturing: A Process-Structure-Property Approach,” Metrology & Inspection, A-FAB T/F, Samsung Electronics, South Korea, Aug. 14, 2024.
  7. Ko, H., “Unraveling Supply Chains: The Transformative Power of Knowledge Graphs,” 2024 Open Industrial Digital Eco System Summit, Tempe, Arizona, Feb. 6, 2024.
  8. Ko, H., “A Framework Driven by Physics-guided Machine Learning of Process-Structure-Property Causality for Digital Additive Manufacturing,” Korea Institute of Machinery & Materials, Korea, Nov. 30, 2022.
  9. Ko, H., “A Framework Driven by Physics-guided Machine Learning of Process-Structure-Property Causality for Digital Additive Manufacturing,” National University of Singapore, Singapore, Nov. 28, 2022.
  10. Ko, H., “Machine-Learning-Driven Spatial-Temporal Modeling for In-Situ Monitoring of Laser Powder Bed Fusion,” Hack3D Symposium, New York University, Jul. 15, 2022
  11. Ko, H., “Machine Learning in Additive Manufacturing: Opportunities and Challenges,” New York University, Jul. 5, 2022
  12. Ko, H., “Design for Additive Manufacturing and Machine-learning-driven Opportunities,” Keynote Speaker, Workshop of Professional Manpower Training for New-materials 3D Printing, Korea Electronics Technology Institute, Gyeongju, Gyeongsangbuk-do, South Korea, Feb. 9, 2022

CONFERENCE-PROCEEDING PRESENTATIONS

(Selected):

Legend: (~) Presenting author | (+) Equal Contributions | Bold Font: Dr. Ko and Dr. Ko’s Ph.D. Student or whom Dr. Ko is the primary advisor | Underline Font: Master’s Student for whom Dr. Ko is the primary advisor |  ‡ High School Student for whom Dr. Ko is the primary advisor

  1. Han, T.~, Taheri, Z., and Ko, H., “Physics-informed neural networks for semiconductor film deposition: A review”, ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2025), Anaheim, California, USA, August 17–20, 2025.
  2. Elhambakhsh, F.~, Grandi, D., and Ko, H., “A domain adaptation of large language models for classifying mechanical assembly components”, ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2025), Anaheim, California, USA, August 17–20, 2025.
  3. Elhambakhsh, F.~, Lee, S., and Ko, H., “Generative multimodal multiscale data fusion for digital twins in aerosol jet electronics printing”, ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2025), Anaheim, California, USA, August 17–20, 2025.
  4. Lee, S.~, and Ko, H., “Generative machine learning in adaptive control of dynamic manufacturing processes: A review”, ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2025), Anaheim, California, USA, August 17–20, 2025 , Paper No. V02BT02A029, ASME.
  5. Pushparajan, R.~, Ameri, F., and Ko, H., “A comparative study of zero-shot multimodal retrieval-augmented generation for image labeling in manufacturing: GPT-4o-Mini vs. Llama 3.2 Vision”, ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2025), Anaheim, California, USA, August 17–20, 2025, Paper No. V02BT02A029, ASME.
  6. Xie, J.~, Safdar, M., Romascanu, A., Lu, Y., Ko, H., Yang, Z., and Zhao, Y.*, “Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing”, ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE 2024), Washington, D.C., USA, August 25–28, 2024, , Paper No. V02AT02A033, ASME.
  7. Lu, Y.~, Xie, J., Safdar, M., Yang, Z., Ko, H., Shengyen, L., Elhambakhsh, F., and Zhao, Y., “An overarching quality evaluation framework for additive manufacturing digital twin”, IEEE 20th International Conference on Automation Science and Engineering (CASE 2024), Bari, Italy, 2024, pp. 676–682.
  8. Lee, S.~, Ko, H., “AM Transformer: A Koopman Theory-Based Transformer to Learn Additive Manufacturing Dynamics,” ASME 2024 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington, DC, USA Aug. 28, 2024
  9. Elhambakhsh, F.~, Ko, H., Yang, Z., Lu, Y., “Denoising Diffusion Probabilistic Modeling Based Causal Data Fusion for Predictive Additive Manufacturing Digital Twins,” ASME 2024 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington, DC, USA Aug. 28, 2024.
  10. Ko, H.~ and Elhambakhsh, F., “A Framework for Physics-guided Machine Learning to Extract and Transfer Process-structure-property Knowledge in Additive Manufacturing,” 34th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, Austin, Texas, USA, Aug. 14-16, 2023.
  11. Elhambakhsh, F.~, and Ko, H., “Machine-learning-driven Digital Twin Construction for Additive Manufacturing,” 34th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, Austin, Texas, USA, Aug. 14-16, 2023.
  12. Ko, H.~, “Explainable Machine Learning for Causality Analytics in Additive Manufacturing,” 2022 INFORMS Annual Meeting, Indianapolis, IN, USA, Oct. 16-19, 2022.
  13. Ko, H.~, Kim, J., Lu, Y., Shin, D., Yang, Z., and Oh, Y., “Spatial-temporal Modeling Using Deep Learning for Real-Time Monitoring of Additive Manufacturing,” ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, St. Louis, Missouri, USA, Aug. 14-17, 2022.
  14. Ko, H.~, “A Review on Machine Learning Interpretation for Additive Manufacturing,” 33rd Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, Austin, Texas, USA, July. 25-27, 2022.
  15. Ko, H.~, Lu, Y., Yang, Z., and Witherell, P., “Being Real-time in Process-structure-property Analytics for Additive Manufacturing using Machine Learning and Knowledge Representation,” Mechanistic Machine Learning and Digital Twins for Computational Science Engineering & Technology, San Diego, CA, USA, Sept. 26-29, 2021.
  16. Ko, H.~, Witherell, P., Ndiaye, N. Y., and Lu, Y., “Machine Learning based Continuous Knowledge Engineering for Additive Manufacturing,” 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, British Columbia, Canada, Aug. 22-26, 2019.
  17. Ko, H.~, Witherell, P., Rosen, D. W., and Kim, S., “A Methodology for Modular Design Rule Representation and Ontology Development for Additive Manufacturing,” 29th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, Austin, Texas, USA, Aug. 13-15, 2018.
  18. Ko, H.~ and Moon, S. K., “Contradicting Functions with Affordances in Design for Additive Manufacturing,” ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Cleveland, Ohio, USA, Aug. 6-9, 2017.
  19. Ko, H.~, Moon, S. K., Wood, K. L., & Oh, H. S., “An Integration of Function- and Affordance-based Methods for Product-service System Utilizing Finite State Automata,” 9th IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, Dec. 4-7, 2016.
  20. Ko, H.~, Sacco, E., Chua, Z. Y., Moon, S. K., and Otto, K., “User-centered Design for Additive Manufacturing as a Customization Strategy,” 2nd International Conference on Progress in Additive Manufacturing (Pro-AM), Singapore, May. 16-19, 2016.
  21. Ko, H.~, Moon, S. K., & Hwang, J., “Design for Additive Manufacturing in Customized Products,” International Symposium on Green Manufacturing and Applications, Busan, South Korea, Jun. 24-28, 2014.
  22. Ko, H.~, Moon, S. K., and Otto, K., “Customization Design Knowledge Representation to Support Additive Manufacturing,” 1st International Conference on Progress in Additive Manufacturing (Pro-AM), Singapore, May. 26-28, 2014.
  23. Ko, H.~ and Shin, D. M., “Affordance-based Interaction Design and Its Implications on Systems Design,” HCI Korea 2012, Alpensia Convention Center, Gangwon-do, Korea, Jan. 11-13, 2012.
  24. Ko, H.~ and Shin, D. M., “s-Scape: a Service Prototype Testing Space for Innovation of Service Quality Improvement,” 2011 IIE Asian Conference AIIE, Shanghai, China, Jun. 10-12, 2011.
  25. Ko, H.~ and Shin, D. M., “Formal Modeling of Quality-Measurable Service Systems using Affordance-based Finite State Automata,” Korean Institute of Industrial Engineers, Seoul, Korea, Nov. 5, 2011.

Service Activities (Selected):

International Standards

Technical and Organizing Committee

Conference and Workshop Session

Editorial Activities

Reviewer

University

Military

Military Police (MP), Korean Augmentation To the United States Army (KATUSA), South Korea