Research team:
Lily Xu, Sun-Wu Assistant Professor at Columbia University in the Department of Industrial Engineering and Operations Research (Lead)
Burcu Balçık, Professor at Özyeğin University
Nabila El-Bassel, University Professor and Willma and Albert Musher Professor of Social Work
Aras Selvi, Assistant Professor of Operations & Technology at the UCL School of Management
After sudden-onset disasters like the 2023 earthquake in Türkiye and Syria, humanitarian responders must act within hours but rarely have a shared picture of who needs what, where, and how urgently. They face a surge of requests from local government, municipalities, civil society organizations, and community representatives through disjointed calls and messages. From this patchwork of unstructured requests, aid organizations must rapidly prioritize which to address given the vast gap between limited resources and the scale of damage, especially in the light of needs of vulnerable groups. Traditional needs assessments take weeks, far too slow when lives hang in the balance.
Our project aims to transform chaotic request streams into structured, decision-ready information for coordinated humanitarian relief. Leveraging machine learning, statistical inference, and optimization, we will develop intelligent decision-support tools that manage uncertainty end-to-end, transforming noisy, chaotic request streams into actionable recommendations. In partnership with the UN World Food Programme (WFP) Türkiye and Hayata Destek, this platform will be designed for humanitarian aid coordination groups to strengthen preparedness for future emergencies, including the expected Istanbul earthquake, and adaptable to disasters beyond Türkiye.
