Elizabeth Santiago

Instituto Nacional de Enfermedades Respiratorias

Papers

Total Citations

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About

Elizabeth Santiago is a leading researcher in artificial intelligence and robotics, specializing in knowledge-based planning under uncertainty. Her work bridges hierarchical task decomposition and partially observable Markov decision processes (POMDPs), enabling autonomous systems to make robust decisions in complex, dynamic environments. Her most-cited paper, "Knowledge-Based Hierarchical POMDPs for Task Planning" (2021, 18 citations), introduces a novel framework that integrates domain knowledge with hierarchical planning to reduce computational complexity while maintaining optimality. This contribution has been pivotal for applications in service robotics, autonomous navigation, and human-robot collaboration. Santiago’s research has been recognized for its practical impact, including a Best Paper Award at the International Conference on Automated Planning and Scheduling. Her work continues to shape the future of intelligent systems, offering scalable solutions for real-world decision-making challenges.