High-stakes decisions from low-quality data: learning and planning for wildlife conservation

[Grigory Bronevetsky]

Modeling Talks

High-stakes decisions from low-quality data: learning and planning for wildlife conservation Thomas Gernon, University of Southampton Tues, September 30, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/high-stakes-decisions-from-low-quality-data-learning-and-planning-for-wild More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home 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, robust reinforcement learning, and diffusion models, 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 Sun-Wu Assistant Professor at Columbia University in the Department of Industrial Engineering and Operations Research. Her research develops AI methods across machine learning, optimization, and causal inference for planetary health challenges, with a focus on biodiversity conservation. She aims to enable practitioners to make effective decisions in the face of limited data, taking actions that are robust to uncertainty, effective at scale, and future-looking. Lily holds a PhD in computer science from Harvard University, and her research has been recognized with best paper runner-up at AAAI, the INFORMS Doing Good with Good OR award, a Google PhD Fellowship, a Siebel Scholarship, and AAMAS best dissertation runner-up.