• Assistant Professor, Industrial and Systems Engineering
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Jaesung Lee headshot

Educational Background

  • Ph.D., Industrial and Systems Engineering, University of Wisconsin – Madison, 2022
  • M.S. Statistics, University of Wisconsin – Madison, 2022
  • M.S. Management Engineering, Korea Advanced Institute of Science and Technology, 2013
  • B.S. Industrial Systems and Information Engineering, Korea University, 2011

Research Interests

  • Dr. Jaesung Lee’s research focuses on statistical modeling and machine learning that integrate domain knowledge, physical laws, and uncertainty quantification for inference, prediction, and decision-making in complex engineering systems. His research spans smart and advanced manufacturing, quality and reliability engineering, and healthcare and biomechanics. Specific research interests include:
    • Bayesian statistical modeling, uncertainty quantification, and domain-informed machine learning
    • Physics-informed machine learning, inverse problems, and digital twins
    • Smart manufacturing, process monitoring, fault diagnosis, quality assurance, and virtual qualification and certification
    • Robust design, process optimization, reliability, and predictive maintenance
    • Biomedical engineering, biomechanics, and healthcare applications

Awards & Honors

  • Best Student Paper Competition Finalist (Ph.D. advisee: Tatthapong Srikitrungruang), INFORMS Quality, Statistics and Reliability (QSR) Section, 2026
  • Montague-Center for Teaching Excellence (CTE) Scholar, Texas A&M University, 2025–2026
  • Best Refereed Paper Competition Finalist, INFORMS Quality, Statistics and Reliability (QSR) Section, 2025
  • Best Student Paper Competition Finalist (Ph.D. advisee: Sina Aghaee Dabaghan Fard), IISE Quality Control and Reliability Engineering (QCRE) Division, 2025
  • IISE Transactions Best Paper Award, Design and Manufacturing, Institute of Industrial and Systems Engineers (IISE), 2024

Selected Publications

  • S. Aghaee Dabaghan Fard, M. Kim, A. Deep, and J. Lee. “Bayesian Joint Model of Multi-Sensor and Failure Event Data for Multi-Mode Failure Prediction,” Technometrics, vol. 68, no. 4, pp. 657–671, 2026.
  • J. Lee, T. Srikitrungruang, and S. Jahani. “Knowledge-Guided Reinforcement Learning for Preventive Maintenance Planning in Economically Dependent Multi-Component Systems,” IISE Transactions, vol. 58, no. 11, pp. 1352–1369, 2026.
  • T. Srikitrungruang, S. Aghaee Dabaghan Fard, M. Lemon, J. Lee, and Y. Zhou. “Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data,” Advanced Science, vol. 12, no. 48, e08445, 2025.
  • J. Lee, S. Zhou, and J. Chen. “Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization,” IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 2039–2050, 2024.
  • J. Lee, C. Wang, X. Sui, S. Zhou, and J. Chen. “Landmark-embedded Gaussian Process with Applications for Functional Data Modeling,” IISE Transactions, vol. 54, no. 11, pp. 1033–1046, 2022. 2024 IISE Transactions Best Paper Award.
  • J. Lee, J. Son, S. Zhou, and Y. Chen. “Variation Source Identification in Manufacturing Processes Using Bayesian Approach with Sparse Variance Components Prior,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1469–1485, 2020.
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