Title: High-stakes decisions from low-quality data:
learning and planning for wildlife conservation
Abstract: Wildlife poaching pushes countless species to the brink of extinction, with animal population sizes declining by an average of 70% since 1970. To aid rangers in preventing poaching in protected areas around the world, we have developed a machine learning system to predict poaching hotspots and plan ranger patrols. In this talk, we present technical advances in multi-armed bandits and robust reinforcement learning, guided by research questions that emerged from on-the-ground challenges in deploying this system. We also discuss bridging the gap between research and practice, from field tests in Cambodia to large-scale deployment through integration with SMART, the leading platform for protected area management used by over 1,200 wildlife parks worldwide.
Bio: Lily Xu is a computer science PhD student at Harvard developing AI techniques to address planetary health challenges. She focuses on advancing methods in machine learning, large-scale planning, and causal inference for biodiversity conservation through preventing wildlife poaching. Her work building the PAWS system to predict poaching hotspots has been deployed in multiple countries and is being scaled globally through integration with SMART conservation software. Lily co-organizes the Mechanism Design for Social Good (MD4SG) research initiative and serves as AI Lead for the SMART Partnership. Her research has been recognized with best paper runner-up at AAAI, the INFORMS Doing Good with Good OR award, a Google PhD Fellowship, and a Siebel Scholarship
