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Our planet has faced rapid biodiversity decline, with animal populations declining by 70% on average since 1970. The Global Biodiversity Framework, signed in 2022, organizes international cooperation for biodiversity conservation, accelerating large-scale action but also sharpening the urgency of unresolved ethical challenges. In this talk, we present technical advances in machine learning, optimization, and causal inference, designed to improve resource allocation for conservation management. We’ll also discuss the sociopolitical considerations of the rise of biodiversity data and AI for conservation.
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.
