报告题目：
Distributionally Robust Group Testing with Correlation Information
报 告 人：香港中文大学 龙卓瑜 教授
报告时间：2025年11月14日（周五） 10:00-11:30
报告地点：文管学馆B306
报告摘要：
Motivated by the need for more efficient and reliable methods of group testing during widespread infectious outbreaks, this paper introduces a novel operational improvement to the widely-used Dorfman's group testing procedure, where a single test is conducted on the pooled sample, followed by individual testing of positive pools. Our method minimizes a weighted sum of testing volume and misclassifications, predicated on prevalence rates and inter-individual Pearson correlation coefficients. Recognizing the inherent ambiguity in the population-level joint probability distribution of infection statuses that rises from the correlations between individuals, our approach leverages a distributionally robust optimization (DRO) framework to counteract the worst-case probability distribution. In the single-cluster case, where each pair of subjects are equally correlated, we establish uniform group sizes and connect our analysis to Nash equilibrium principles. Larger testing groups are generally favored under high correlation, whereas individual testing becomes optimal under high prevalence. In the multi-cluster case, where the population is formed by several intra-correlated but inter-independent clusters, we highlight the effectiveness of mixed-cluster testing strategies, particularly at lower levels of prevalence and correlation. This is a notable addition to the prevailing view that advocates pooling correlated individuals. %Conversely, scenarios with high prevalence or high correlation tend to favor individual testing or same-cluster pooling. For both single- and multi-cluster cases, we develop polynomial-time solutions and offer practical insights and policy implications for optimal pooling strategies. We demonstrate the trade-offs and benefits of our DRO approach through a thorough comparison with stochastic alternatives, and demonstrate the impact of incorporating correlated infections in a case study using a COVID-19 dataset from Hong Kong.
报告人简介：
Daniel Zhuoyu Long is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. Previously, he received his Bachelor's degree from Tsinghua University in 2005, Master's degree from the Chinese Academy of Sciences in 2008, and Ph.D. from the National University of Singapore Business School in 2013, joining CUHK in the same year. His research primarily focuses on distributed robust optimization theory and its applications to various operations management problems, such as logistics and supply chain management, project management, healthcare operations management, and revenue management. His work was elected as a finalist for the 2021 Best OM Paper in OR, and received the 2022 CSAMSE Best Paper Award (First Prize) and 2024 CSAMSE Best Paper Award (Second Prize). He currently serves as an Associate Editor for the MSOM Journal.
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