Lily Xu: High-stakes decisions from low-quality data: AI decision-making for conservation

Like many of society's grand challenges, biodiversity conservation requires effectively allocating and managing our limited resources in the face of imperfect information. 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 machine learning, reinforcement learning, and causal inference, addressing research questions that emerged from on-the-ground challenges in wildlife conservation. I’ll also discuss bridging the gap from research and practice, with anti-poaching field tests in Cambodia, field visits in Belize and Uganda, and large-scale deployment with SMART conservation software.

Lily Xu is a computer scientist developing methods across machine learning, optimization, and causal inference for environmental management. 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. 

She is currently a postdoctoral research fellow with the Leverhulme Centre for Nature Recovery at Oxford and will begin as an assistant professor at Columbia University in fall 2025. 

Lily also serves as AI lead for the SMART Partnership, supporting rangers in protected areas worldwide, and co-organizes the EAAMO research initiative, committed to advancing Equity and Access in Algorithms, Mechanisms, and Optimization.