Research
IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy systems, robotics, and management.
Selected Publications
Dynamic Pricing with External Information and Inventory Constraint
Li, Xiaocheng & Zheng, Zeyu. (2023). Dynamic Pricing with External Information and Inventory Constraint. Management Science. 10.1287/mnsc.2023.4963.
Behavioral Analytics for Myopic Agents
Mintz, Yonatan & Aswani, Anil & Kaminsky, Philip & Fukuoka, Yoshimi. (2017). Behavioral Analytics for Myopic Agents. European Journal of Operational Research. 310. 10.1016/j.ejor.2023.03.034.
Stochastic Localization Methods for Convex Discrete Optimization via Simulation
Zhang, Haixiang & Zheng, Zeyu & Lavaei, Javad. (2023). Stochastic Localization Methods for Convex Discrete Optimization via Simulation. Operations Research. 10.1287/opre.2022.0030.
Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards
Dogan, Ilgin & Shen, Max & Aswani, Anil. (2023). Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards. 10.48550/arXiv.2308.06717.
No Panic in Pandemic: The Impact of Individual Choice on Public Health Policy and Vaccine Priority.
Bai, Miao and Cui, Ying and Kong, Guangwen and Zhang, Zhenhuan, No Panic in Pandemic: The Impact of Individual Choice on Public Health Policy and Vaccine Priority (January 10, 2021). University of Connecticut School of Business Research Paper No. 21-02, Available at SSRN: https://ssrn.com/abstract=3763514 or http://dx.doi.org/10.2139/ssrn.3763514
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
Zhang, Y., Cui, Y., Sen, B., & Toh, K. (2022). On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models. ArXiv. /abs/2208.07514