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I am an Assistant Professor in the Department of Industrial & Systems Engineering (ISEN) at Texas A&M University. I received my Ph.D. in Industrial Engineering from the University of Wisconsin - Madison.
My research establishes domain-informed statistical and machine learning methods for engineering systems under uncertainty. I integrate physical laws and other domain knowledge with imperfect data to infer unobserved system properties, quantify uncertainty, and enable prediction and decision-making.
My current research focuses on Bayesian modeling and uncertainty quantification, physics-informed machine learning for inverse problems, and digital twin systems. These methods are applied to critical problems in advanced manufacturing and healthcare and biomechanics, including patient-specific prediction and evaluation, inference of heterogeneous physical properties, prognostics and reliability, and data-driven engineering decision-making.
Specifically, my research topics include
Domain-informed statistical machine learning and uncertainty quantification
Bayesian inference and physics-informed machine learning for inverse problems
Digital twins and predictive analytics
Prognostics, reliability, and decision-making under uncertainty
Applications in advanced manufacturing and healthcare/biomechanics
Please see the research page for details.
If you want to discuss collaboration or research ideas, please feel free to email me (j.lee at tamu.edu).
If you are interested in a Ph.D. position, please email me (j.lee at tamu.edu) with your curriculum vitae and transcripts (undergraduate and Master's if applicable). Optionally, you can also provide your state of purpose and research publications/projects you worked on. Qualifications include strong mathematical and/or statistical backgrounds. Strong coding skills are preferred.
Link: (Google scholar) (Linkedin)
My research group has been working with industry collaborators and supported by multiple institutions:
Industrial Research Collaborators:
- EOS North America, 3Degrees
Funding Partners:
- NSF, NIH, LIFT, U.S. Department of War/Defense, TAMIDS, Crider Foods
Education
PhD 2022 Industrial & Systems Engineering University of Wisconsin–Madison, Madison, WI
MS 2020 Statistics University of Wisconsin–Madison, Madison, WI
MS 2013 Management Engineering KAIST, Seoul, South Korea
BFE 2011 Financial Engineering Korea University, Seoul, South Korea
BS 2011 Industrial Systems Information Engineering Korea University, Seoul, South Korea
Research Interests
Advanced Statistical modeling and data mining methods in the application of advanced Engineering processes and systems.
Data-driven modeling, analysis, design, and control of emerging advanced manufacturing processes
Data-driven analytics for IoT-enabled smart manufacturing, service, and healthcare systems
Courses Taught
ISEN 350 Quality Engineering
ISEN 614 Advanced Quality Control
DAEN 210 Uncertainty Modeling
I have developed and offered DAEN 210 for the Data Engineering program newly offered by the department.
Awards and Honors
2025–26 Montague-Center for Teaching Excellence (CTE) Scholar, Texas A&M University, 2025
University-wide recognition for excellence and innovation in undergraduate teaching (includes a $6,500 teaching grant)
QSR Best Referred Paper Competition Finalist, INFORMS Annual Meeting, 2025
QCRE Student Paper Competition Finalist (Ph.D. Student: Sina Aghaee), IISE Annual Conference & Expo, 2025
IISE Transactions Best Paper Award, IISE, 2024
Best paper award, Management and Innovation Technology International Conference, 2015
Best Master’s Thesis Award, KAIST, 2013
Research Recognition
Recognized as a top-viewed article, Advanced Materials Technologies, 2023.
Featured Article in ISE Magazine, IISE, 2022
Google Sites
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