Julie Lee

Education

• PhD, Operations Research, Georgia Institute of Technology, 2025.
• MS, Operations Research, Georgia Institute of Technology, 2022.
• BA, Engineering Sciences, Dartmouth College, 2018.
• BEng, Mechanical Engineering, Dartmouth College, 2018.

Faculty Research Interests

• Healthcare applications of operations research
• Trustworthy machine learning and artificial intelligence
• Stochastic optimization and robust optimization

Recent and Representative Publications

Lee, S., Yang, Y., Liu, T., Liao, C., Keyvanshokooh, E., Shao, H., Pasquel, F., Weber, B. M., Garcia, P. G., (2026) Development and Evaluation of Cardiovascular Disease Risk Prediction Models for Patients with Type 2 Diabetes. Scientific Reports. https://doi.org/10.1038/s41598-026-45129-5

Lee, S., Gong, X., Garcia, G., (2025) Modified monotone policy iteration for interpretable policies in Markov decision processes and the impact of state ordering rules. Annals of Operations Research. 347(2), 783-841. https://doi.org/10.1007/s10479-024-06158-3

Lee, S., Pandey, S. H., Garcia, P. G., (2023) Designing Interpretable Machine Learning Models using Mixed Integer Programming. 1-8. https://doi.org/10.1007/978-3-030-54621-2_867-1 Springer International Publishing.

Lee, S., Garcia, P. G., Stanhope, K. K., Platner, H. M., Boulet, L. S., (2023) Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy. American Journal of Obstetrics & Gynecology MFM. 5(10), 101096. https://doi.org/10.1016/j.ajogmf.2023.101096