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 article 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 (i) increase participant satisfaction as measured by the post-JSM satisfaction survey, and (ii) 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）有时会令人颇感困扰，因同一主题的多个分会场常会被安排在同一时段。 本文采用种子潜在狄利克雷分配（seeded latent Dirichlet allocation）与调度优化算法，对2020年联合统计会议的原始日程进行优化，大幅降低了内容重叠问题。 具体而言，本文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）。原始日程的得分仅为0.058，而本文提出的优化后日程得分可达0.371。 这一改进效果极为显著：其一，可通过会后满意度调研（post-JSM satisfaction survey）提升参会者满意度；其二，无需再召集47名程序委员会主席及其他组织者开展传统线下会议，可为美国统计协会（American Statistical Association）节省可观的运营经费。 本文提出的方法可直接应用于后续各届联合统计会议，且经简单修改后，即可用于优化其他设有并行分会场的学术会议的日程安排。