Download the current CV (PDF) · Updated October 23, 2026
Operations research and industrial engineering, with a focus on data-driven optimization for healthcare operations and public health decision-making. Research areas include optimization under uncertainty; combinatorial and network optimization; Markov decision processes and approximate dynamic programming; and optimization for data analytics. Applications include healthcare scheduling and resource allocation, screening and diagnostic strategies, and infectious disease prevention and control.
Li, S., Aprahamian, H., & Chatterjee, S. (2026). A convex relaxation-based spatial branching approach for optimal robust group testing designs under prevalence rate and dilution behavior uncertainty. INFORMS Journal on Computing. Article in Advance.
Li, S., & Aprahamian, H. (2024). An optimization-based framework to minimize the spread of diseases in social networks with heterogeneous nodes. IISE Transactions, 56(2), 128–142.
Li, S., & Aprahamian, H. (2024). Quantifying the benefits of customized vaccination strategies: A network-based optimization approach. Naval Research Logistics, 71(1), 64–86.
Li, S., Aprahamian, H., Nouiehed, M., & El-Amine, H. (2024). An optimization-based order-and-cut approach for fair clustering of data sets. INFORMS Journal on Data Science, 3(2), 124–144.
Barth, J., Li, S., Aprahamian, H., & Gupta, D. (2024). Spatiotemporal vaccine allocation policies for epidemics with behavioral feedback dynamics. Naval Research Logistics, 71(1), 109–139.
Li, S., Ciré, A., Diamant, A., & Sarhangian, V. (2026). Revealing state-relevance weights in dual approximate linear programming. Submitted to Operations Research.
Li, S., Schaefer, A. J., Jaffray, D. A., Siewerdsen, J. H., & Yung, J. P. (2026). Digital twin and optimization-guided MRI patient sequencing to reduce changeover and increase appointment-slot availability. Submitted to JCO Oncology Practice.
— Selected for Best of Professional at AAPM 2026 (American Association of Physicists in Medicine); accepted at RSNA 2026 (Radiological Society of North America) Annual Meeting.
Li, S., Jang, I., Barbon, C. E. A., Moreno, A. C., Schaefer, A. J., Lee, J. J., Lai, S. Y., Fuller, C. D., Hutcheson, K. A., & Manduchi, B. (2026). Tumor burden and early swallow recovery predict imaging-graded dysphagia at 2- and 5-years after treatment for oropharyngeal cancer. Submitted to Radiotherapy & Oncology.
Li, S., Toumazis, I., Schaefer, A., Ishizawa, S., Ozik, J., Collier, N. T., Meza, R., Lam, S., & Khattra, S. (2026). A decision-analytic model to optimize diagnostic management for screen-detected pulmonary nodules. Working paper.
— Accepted at WCLC 2026 (World Conference on Lung Cancer).
Li, S., Powers, S., & Schaefer, A. J. (2026). Optimizing automated ball-strike challenges in Major League Baseball: A win-probability Markov decision model. Working paper.
Li, S., & Schaefer, A. J. (2026). Shape-constrained approximate linear programming for integer-resource Markov decision processes via Chvátal–Gomory basis functions. Working paper.
Li, S., Toumazis, I., & Schaefer, A. J. (2026). State-action-induced nested Markov decision processes: Boundary-price decomposition and policy-column algorithms. Working paper.
Conference session chair
Journal peer reviewer
INFORMS Journal on Data Science; Health Care Management Science; INFOR: Information Systems and Operational Research; Scientific Reports; AI and Ethics; Journal of King Saud University—Computer and Information Sciences; Discover Global Society.