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Abstract: Our planet faces growing crises including biodiversity loss, with animal population sizes declining by 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, often in dynamic environments with limited data. Many of these challenges can be modeled as restless bandits, which traditionally require a perfect model of the environment and relatively small problem sizes. We’ll explore how online learning, deep reinforcement learning, and mixed-integer programming can help overcome these challenges of missing data and complexity. Bio: Lily is a postdoc at Oxford and this fall will join Columbia IEOR as an assistant professor. Her research develops methods across machine learning, optimization, and causal inference for decision-making under uncertainty, informed by real-world challenges in biodiversity conservation and other planetary challenges.