headshot of Taewoo Lee

Taewoo Lee

Adjunct Assistant Professor
taewoo.lee@pitt.edu
Industrial Engineering

overview

Dr. Taewoo Lee is an Assistant Professor of Industrial Engineering at the University of Pittsburgh. His general research interests are in the applications of optimization techniques such as inverse optimization, robust optimization, and dynamic programming to healthcare operations and medical decision making. Specific application areas of interest include radiation therapy, organ transplantation, and screening for diabetic complications. His research has been supported by the National Science Foundation and the Centers for Medicare and Medicaid Services. Dr. Lee received a Ph.D. degree in Operations Research from the Universe of Toronto.

about

PhD, University of Toronto, 2015

MS, University of Toronto, 2010

BS, Korea University, 2008

Nadi, S., Lee, T., & Prokopyev, O.A. (2025). The inverse optimal value problem for linear fractional programming. OPERATIONS RESEARCH LETTERS, 59, 107251.Elsevier. doi: 10.1016/j.orl.2025.107251.

Mildebrath, D., Lee, T., Sinha, S., J., A., & Gaber, A.O. (2024). Characterizing Rational Transplant Program Response to Outcome-Based Regulation. OPERATIONS RESEARCH, 72(4), 1421-1437.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2018.0721.

Ajayi, T., Lee, T., Schaefer, A.J., Lee, T. (2023). A note on the implications of approximate submodularity in discrete optimization. Optimization Letters, 17(1), 1-26.

Dorali, P., Shahmoradi, Z., Weng, C.Y., & Lee, T. (2023). Cost-effectiveness Analysis of a Personalized, Teleretinal-Inclusive Screening Policy for Diabetic Retinopathy via Markov Modeling. Ophthalmology Retina, 7(6), 532-542.Elsevier. doi: 10.1016/j.oret.2023.01.001.

Ajayi, T., Lee, T., & Schaefer, A.J. (2022). Objective Selection for Cancer Treatment: An Inverse Optimization Approach. Operations Research, 70(3), 1-22.

Shahmoradi, Z., & Lee, T. (2022). Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming. Operations Research, 70(4), 2538-2562.

Shahmoradi, Z., & Lee, T. (2022). Optimality-based clustering: An inverse optimization approach. Operations Research Letters, 50(2), 205-212.Elsevier. doi: 10.1016/j.orl.2021.12.012.

Babier, A., Chan, T.C.Y., Lee, T., Mahmood, R., & Terekhov, D. (2021). An Ensemble Learning Framework for Model Fitting and Evaluation in Inverse Linear Optimization. INFORMS Journal on Optimization, 3(2), 119-138.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/ijoo.2019.0045.

Chan, T.C.Y., Lee, T., & Terekhov, D. (2019). nverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit. Management Science, 1115-1135. doi: 10.1287/mnsc.2017.2992.

Chan, T.C.Y., & Lee, T. (2018). Trade-off preservation in inverse multi-objective convex optimization. European Journal of Operational Research, 270(1), 25-39.Elsevier. doi: 10.1016/j.ejor.2018.02.045.

Ghobadi, K., Lee, T., Mahmoudzadeh, H., & Terekhov, D. (2018). Robust inverse optimization. Operations Research Letters, 46(3), 339-344.Elsevier. doi: 10.1016/j.orl.2018.03.007.

Tavashoglu, O., Lee, T., Valeva, S., & Schaefer, A.J. (2018). On the structure of the inverse-feasible region of a linear program. Operations Research Letters, 46(1), 147-152.Elsevier. doi: 10.1016/j.orl.2017.12.004.

Boutilier, J.J., Lee, T., Craig, T., Sharpe, M.B., & Chan, T.C.Y. (2015). Models for predicting objective function weights in prostate cancer IMRT. Medical Physics, 42(4), 1586-1595.Wiley. doi: 10.1118/1.4914140.

Chan, T.C.Y., Craig, T., Lee, T., & Sharpe, M.B. (2014). Generalized Inverse Multiobjective Optimization with Application to Cancer Therapy. Operations Research, 62(3), 680-695.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2014.1267.

Lee, T., Hammad, M., Chan, T.C.Y., Craig, T., & Sharpe, M.B. (2013). Predicting objective function weights from patient anatomy in prostate IMRT treatment planning. Medical Physics, 40(12), 121706.Wiley. doi: 10.1118/1.4828841.