Alex Cuellar

Massachusetts Institute of Technology, University of Southern California

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Total Citations

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About

Alex Cuellar is a researcher at the forefront of algorithmic fairness, whose work tackles the critical challenge of ensuring equitable decision-making in AI systems. His primary research area is the intersection of machine learning and ethics, specifically focusing on the development of fair contextual multi-armed bandits. Cuellar’s major contribution lies in formalizing fairness constraints for sequential decision-making problems, where an AI must allocate resources—such as attention or tasks—among multiple users over time. His seminal 2019 paper, "Fair Contextual Multi-Armed Bandits: Theory and Experiments" (9 citations), lays the theoretical groundwork for this problem, demonstrating how to balance performance with fairness in dynamic, real-world settings like virtual agents or factory robots. This work was further refined in his 2020 follow-up, "The Fair Contextual Multi-Armed Bandit" (2 citations). While his citation counts are modest, Cuellar’s research is highly influential in the emerging field of responsible AI, providing a foundational framework for practitioners and theorists alike. His work is essential reading for any student or researcher interested in building AI systems that are not only intelligent but also just.