High-stakes decisions from low-quality data: AI decision-making for planetary health
Planetary health is an emerging field which recognizes the inextricable link between human health and the health of our planet. Our planet’s growing crises include biodiversity loss, with animal population sizes declining by an average of 70% since 1970, and maternal mortality, with 1 in 49 girls in low-income countries dying from complications in pregnancy or birth. Underlying these global challenges is the urgent need to effectively allocate scarce resources. My research develops data-driven AI decision-making methods to do so, overcoming the messy data ubiquitous in these settings. Here, I’ll present technical advances in stochastic bandits, robust reinforcement learning, and restless bandits, addressing research questions that emerge from my close collaboration with the public sector. I’ll also discuss bridging the gap from research and practice, including anti-poaching field tests in Cambodia, field visits in Belize and Uganda, and large-scale deployment with SMART conservation software.
Speaker Biography
Lili Xu (University of Oxford, Leverhulme Centre for Nature Recovery)
Postdoc
Lily Xu is postdoc at the University of Oxford with the Leverhulme Centre for Nature Recovery, working with Alex Teytelboym, developing AI techniques to address planetary health challenges. She focuses on advancing methods in machine learning, large-scale planning, and causal inference. 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. Lily Xu I will join as an assistant professor at Columbia University in fall 2025, in the department of Industrial Engineering and Operations Research.