Events

Mr. Huifeng Su
Ph.D. Candidate in Operations Management
Yale University

Over 100,000 victims rely on self-reported, semi-structured clues on humanitarian service platforms to search for their missing parent or child through text comparisons. However, such searches face significant challenges, including vast search spaces, inaccurate data reporting, and complex matching patterns among text pairs. Despite its societal importance, the operational challenges inherent in these large-scale search efforts remain understudied. We analyze structured and unstructured data from a large online family-reunification forum and develop a novel deep learning–based recommendation system that effectively handles these challenges. Our approach significantly narrows search spaces and improves human search quality and efficiency, outperforming existing state-of-the-art solutions, including LLM-based approaches. We show that, when tailored and combined appropriately, smaller but domain-adapted models can deliver superior performance while remaining fast, cost-free, and locally deployable for non-profit organizations. Beyond directly enhancing search quality and efficiency, we demonstrate that text-comparison–based recommendations can also improve DNA collection compliance, creating new opportunities for the design and operation of family-reunification services.