Optimizing the JSM Program

Sometimes the Joint Statistical Meetings (JSM) is frustrating to attend, because multiple sessions on the same topic are scheduled at the same time. This paper uses seeded Latent Dirichlet Allocation and a scheduling optimization algorithm to very significantly reduce overlapping content in the original schedule for the 2020 JSM program. Specifically, a measure based on total variation distance that ranges from 0 (random scheduling) to 1 (no overlapping content) finds that the original schedule had a score of 0.058, whereas our proposed schedule achieved a score of 0.371. This is a huge improvement that would (1) increase participant satisfaction as measured by the post-JSM satisfaction survey, and (2) save the American Statistical Association significant money by obviating the need for the traditional in-person meeting of the 47 program chairs and other organizers. The methodology developed in this work immediately applies to future JSMs and is easily modified to improve scheduling for any other scientific conference that has parallel sessions.参加联合统计会议（Joint Statistical Meetings，JSM）时常令人倍感困扰，原因在于同一主题的多场分会会被安排在同一时段。本文采用带种子的潜在狄利克雷分配（Latent Dirichlet Allocation）与日程优化算法，显著减少了2020年联合统计会议原始日程中的内容重叠问题。具体而言，我们采用基于总变差距离（total variation distance）的评价指标，其取值范围为0（随机安排日程）至1（无内容重叠）。原始日程的得分为0.058，而本文提出的优化方案得分可达0.371。这一改进效果显著：其一，可通过会后满意度调研提升参会者满意度；其二，美国统计协会（American Statistical Association）可免去召集47位分会主席及其他组织者线下参会的传统流程，从而节省大量经费。本文所提出的方法可直接应用于后续联合统计会议，且易于调整，用于优化其他设有平行分会的学术会议的日程安排。