讲座嘉宾：杜理论 副教授

讲座时间：2026年4月3日（周五）上午10:00

讲座地点：重庆大学沙坪坝校区B校园经济与工商管理学院103教室

嘉宾简介：Dr. Lilun DU is an associate professor of department of Decision Analytics and Operations, College of Business, City University of Hong Kong (CityUHK). He received his PhD in Statistics from University of Wisconsin-Madison in Year 2015. He is currently the program director of Master of Business and Data Analytics (Quantitative Analysis for Business) at CityUHK. He has broad research interests in multiple hypotheses testing, empirical Bayes method, and econometrics. Dr. DU also works in Operation Analytics and has derived data-driven tools for solving problems in high-volume recruitment. His work has been published in Annals of Statistics, Journal of American Statistical Association, Manufacturing & Services Operations Management， and Operations Research.

讲座摘要：A firm needs to select applicants from an applicant pool to fill a number of identical job positions. Each applicant in the pool has an initial score. The firm can select applicants based on these initial scores but can also conduct costly tests to learn more about the applicants and obtain test scores.
    We develop a model framework to facilitate decision-making in this context. Under an intuitive condition, there are two cutoffs: applicants with initial scores higher than the upper cutoff should be accepted, those with scores lower than the lower cutoff should be rejected, and those with scores in between the cutoffs should be shortlisted for testing. As the tests become more informative, the upper cutoff increases, and the lower cutoff decreases. The shortlisted applicants are ranked based on their initial and test scores, and how these scores affect the ranking depends on their relationship to the applicants' qualifications.
    We compare the optimal policy with two commonly used policies in practice. Both are easier to compute than the optimal policy and provide useful bounds that facilitate the computation of the optimal policy. We evaluate our method numerically and demonstrate its practical relevance through a case study. Overall, our model framework is general and applicable to contexts such as recruitment, loan approvals, medical triage, and startup funding. This is a joint work with Chenyin GONG and Qing LI from HKUST.