My recent research focuses on improving decision-making in healthcare and pharmaceutical operations, with a special emphasis on site selection, clinical trial design, and supply chain management. Clinical trials are essential for bringing new therapies to patients, but inefficiencies in site selection, recruitment, and logistics can delay treatments and increase costs. I develop optimization, simulation, and machine learning models that help sponsors and researchers design better trials, allocate resources effectively, and adapt to uncertainty.
The process of clinical trials for new medical treatments is a challenging undertaking. The development of the treatments themselves is an expensive and time-consuming task. Beyond that is the more subtle issue of evaluating the effectiveness of any such treatment. Poorly designed management of those trials is not just expensive, it can lead to the collection of useless data. These challenges drive my research to create solutions that make clinical trials faster, more reliable, and less costly.
(*: undergraduate students)
Ninh, A., Gregory, H., and Nguyen, D. , 2025. Estimation of Patient Recruitment Using Summary Data Aggregated Across Trials. Accepted at INFORMS Journal on Computing, https://doi.org/10.1287/ijoc.2024.0780.cd
Ninh, A., Bao, Y., Mcgibney, D. and Nguyen, T., 2024. Clinical trial site selection with probabilistic constraints. European Journal of Operations Research, 316(2), 779-791.
Rubio-Herrero, J., Ninh, A. and Lefew, M., 2023. Improving the performance of supply chains in clinical trials with delays: an optimization approach to determining the number of recruitment sites. Annals of Operations Research, 1-21.
Ninh, A., Melamed, B. and Zhao, Y., 2020. Analysis and optimization of recruitment stocking problems. Annals of Operations Research, 295(2), 747-767.
Lefew, M., Ninh, A. and Anisimov, V., 2020. End-to-end drug supply management in multi-center trials. Methodology and Computing in Applied Probability, pp.1-15.
Ninh, A., LeFew, M. and Anisimov, V., 2019, December. Clinical trial simulation: Modeling and practical considerations. In 2019 winter simulation conference (WSC) (118-132). IEEE.
Fleischhacker, A., Ninh, A. and Zhao, Y., 2015. Positioning inventory in clinical trial supply chains. Production and Operations Management, 24(6), 991-1011.
Clinical Trial Insight, December 2020: "Moving targets".
My research also develops models and methods at the intersection of optimization and machine learning, integrating stochastic modeling and predictive analytics to design solutions that are both rigorous and practical. I am particularly interested in theoretical properties, such as log-concavity, that ensure tractability and guide the design of efficient algorithms.
(*: undergraduate students)
Zhu, X., Ninh, A., Zhao, H. and Liu, Z.M., 2021. Demand forecasting with supply‐chain information and machine learning: Evidence in the pharmaceutical industry. Productions and Operations Management, 30(9), 3231-3252.
Ninh, A., 2021. Robust newsvendor problems with compound Poisson demands. Annals of Operations Research, 302(1), 327-338.
Ninh, A., Shen, Z.J.M. and Lariviere, M.A., 2020. Concavity and Unimodality of Expected Revenue Under Discrete Willingness to Pay Distributions. Production and Operations Management, 29(3), 788-796.
Pham, M., Ninh, A., Le, H. and Liu, Y., 2020. An efficient algorithm for minimizing multi non-smooth component functions. Journal of Computational and Graphical Statistics,1-9.
Ninh, A., Hu, H. and Allen, D*., 2019. Robust newsvendor problems: Effect of discrete demands. Annals of Operations Research, 275(2), 607-621.
Prékopa, A., Ninh, A. and Alexe, G., 2016. On the relationship between the discrete and continuous bounding moment problems and their numerical solutions. Annals of Operations Research, 238(1-2), 521-575.
Ninh, A. and Prékopa, A., 2013. Log-concavity of compound distributions with applications in stochastic optimization. Discrete Applied Mathematics, 161(18), 3017-3027.