Assistant Professor
Phone: 903.565.5711
Email: mko@uttyler.edu
Building: RBS
Department: Civil and Construction Engineering and Management
Minhyeok Ko
Biography
Minhyeok Ko is an Assistant Professor in Civil Engineering at The University of Texas
at Tyler. His research advances the resilience of civil infrastructure systems by
integrating physics-based modeling with machine learning to develop digital twin frameworks
for structural health monitoring, state estimation, and decision-making under uncertainty.
By bridging structural mechanics, Bayesian filtering, and data-driven inference, his
work contributes to real-time monitoring and predictive modeling of complex structural
systems subjected to natural hazards and aging. His research areas include digital
twin frameworks, structural health monitoring, uncertainty quantification, and physics-informed
machine learning.
Before joining UT Tyler, he was a Postdoctoral Research Associate at The Ohio State University, where he developed physics-based deep learning approaches for digital twins of wind turbines and civil infrastructure. He earned his Ph.D. in Civil Engineering from Penn State University, where his dissertation focused on advancing computational methods for uncertainty quantification and nonlinear structural modeling.
Degrees
· Ph.D, Civil and Environmental Engineering, Penn State University, 2023
· M.S., Civil and Environmental Engineering, Northwestern University, 2015
· M.S., Architectural Engineering (Structure), Chung-Ang University, 2014
· B.S., Architectural Engineering (Structure), Chung-Ang University, 2012
Professional Appointments
· Assistant Professor, The University of Texas at Tyler, 2025 – present
· Instructor, The Ohio State University, 2024 – 2025
· Postdoctoral Research Associate, The Ohio State University, 2024 – 2025
Research Areas of Interest
· Digital Twin for civil infrastructure
· Structural health monitoring and system identification
· Uncertainty quantification and risk-informed decision-making
· Physics-informed and data-driven modeling
· Resilience assessment of infrastructure systems under natural hazards
· Computational physics-based modeling
Selected Publications
Peer-Reviewed Journal Articles
Ko, M., & Shafieezadeh, A. (2025). Robust wind turbine monitoring for digital twin
integration: A physics-informed covariance-preserving deep learning approach. Renewable
Energy. https://doi.org/10.1016/j.renene.2025.123176
Ko, M., & Papakonstantinou, K. (2025). The quadratic point estimate method for probabilistic
moments computation. Probabilistic Engineering Mechanics. https://doi.org/10.1016/j.probengmech.2024.103705
Ko, M., & Shafieezadeh, A. (2024). Cholesky–KalmanNet: Deep learning–based Kalman
filters estimating state and error covariance. IEEE Signal Processing Letters. https://ieeexplore.ieee.org/document/10804573
Lee, S., Kim, Y., Ko, M., & Lee, C. (2019). Assessment of thermal prestress loss with
re-tensioning tests. ACI Structural Journal, 116(6).
Lee, C., Ko, M., & Lee, Y. (2014). Bend strength of complete closed-type carbon fiber–reinforced
polymer stirrups with rectangular section. Journal of Composites for Construction,
18(1), 04013022. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000428
Conference Proceedings
Ko, M., & Shafieezadeh, A. (2025). Deep learning–based digital twin models for real-time
monitoring of renewable energy systems. Career Speaker Series & Research Poster Session,
Byrd Polar and Climate Research Center, The Ohio State University, Columbus, OH, USA.
Ko, M., & Shafieezadeh, A. (2025). Deep learning–based digital twin models for real-time
monitoring of onshore wind turbines. NHERI Computational Symposium, University of
California, Los Angeles, CA, USA.
Ko, M., & Papakonstantinou, K. (2023). Copula-based quadratic point estimate method
for probabilistic moments evaluation. ICASP14, Trinity College Dublin, Ireland.
Ko, M., & Papakonstantinou, K. (2023). Copula-based quadratic point estimate method
under incomplete probability information. EMI 2023, Georgia Institute of Technology,
Atlanta, GA, USA.
Ko, M., & Papakonstantinou, K. (2022). An efficient and accurate point estimate method
for probabilistic moments evaluation. EMI 2022, Johns Hopkins University, Baltimore,
MD, USA.
Ko, M., Memari, A. M., Duarte, J. P., Nazarian, S., Ashrafi, N., Craveiro, F., & Bilen,
S. (2018). Preliminary structural testing of a 3D-printed small concrete beam and
finite element modeling of a dome structure. The 42nd IAHS World Congress, Naples,
Italy.
Ko, M., & Lee, C. (2014). Bend strength of complete closed-type CFRP stirrups with
rectangular section. 2013 Spring Conference – KSMI, Busan, South Korea.
Ko, M., & Lee, C. (2013). Estimation of prestress loss due to steam curing. 2013 Spring
Conference – KSMI, Busan, South Korea.
Ko, M., & Lee, C. (2012). Theoretical prediction of prestress loss during steam curing
process. 2012 Fall Conference – KCI, Gangwon, South Korea