Abstract
Motivated by applications in social services and beyond, we study data-driven policies for the online allocation of scarce resources—such as housing, wireless bandwidth, or cloud computing resources—to heterogeneous individuals or agents under uncertainty. Leveraging observational data collected in deployment, we develop a unified framework that integrates predictive modeling and optimization to learn allocation policies that maximize long-run outcomes subject to capacity and fairness constraints. Our approach constructs simple, interpretable waitlist-based policies that balance individual benefit with the opportunity cost of limited resources and achieve strong asymptotic optimality guarantees.
A key challenge in these systems is that distribution shifts arise from e.g., evolving measurement processes or changes in user behavior, altering both covariate distributions and their relationship with outcomes between training and deployment. We address this using a distributionally robust optimization approach that hedges against such shifts while allowing practitioners to tune trade-offs between efficiency and robustness.
Empirical results on real-world homelessness data demonstrate significant improvements in allocation outcomes and robustness with minimal cost of fairness. We further discuss how the proposed framework extends to engineering domains, including network resource allocation in communication systems, or scheduling in cloud computing platforms.
Bio
Carmen Amo Alonso is a Schmidt Science Fellow affiliated with the Autonomous Systems Lab (led by Professor Marco Pavone) at Stanford University. Her research lies at the intersection of control theory, optimization, and artificial intelligence (AI) with a focus on the principled design of reliable and controllable AI technologies. Amo Alonso’s work seeks to adapt mathematical principles from control theory, traditionally used to ensure safety and predictability in engineered systems, to understand, control, and ultimately improve the behavior of AI systems, including generative models for language applications and embodied systems. The impact of her research has led to various collaborations with industry, including Google and Apple. At Stanford, Amo Alonso was named an Emerson Consequential Scholar for the potential of her research to positively impact society. Prior to joining Stanford, she was a Fellow at the Artificial Intelligence Center at ETH Zurich. Amo Alonso earned her doctoral degree in control and dynamical systems from Caltech in 2023, where she was advised by Professor John Doyle. Her thesis was awarded the Milton and Francis Clauser Doctoral Prize, which recognizes the best doctoral dissertation of the year across all disciplines at Caltech. During her doctorate, her research received two IEEE best paper awards, was partially funded by Amazon and D. E. Shaw fellowships, and earned her three Rising Star titles (EECS, Cyber-Physical Systems, and Brain and Cognitive Sciences). Besides her research collaborations across academia and industry, Amo Alonso is committed to education for all. As a member of Clubes de Ciencia, she travels to Mexico in the summer to teach underserved students.
