Alicia Esquivel Morel

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About

Alicia Esquivel Morel is a leading researcher at the intersection of cloud computing, robotics, and autonomous systems, with a particular focus on the emerging "edge to cloud continuum." Her most cited work, "AutoLearn: Learning in the Edge to Cloud Continuum" (2023), addresses the critical challenge of distributing machine learning workloads across heterogeneous computing environments—from local edge devices to centralized cloud servers. This paper, which has already garnered 4 citations in its first year, proposes novel frameworks for automated resource allocation and model adaptation in dynamic, latency-sensitive scenarios. Morel's contributions are particularly notable for their emphasis on hands-on, practical experimentation, leveraging National Science Foundation (NSF)-supported testbeds to validate her approaches in real-world settings. Her research bridges the gap between theoretical distributed systems and applied autonomous robotics, enabling more efficient, responsive, and scalable AI deployments. By pioneering methods that allow autonomous systems to learn and adapt across the computing continuum, Morel is shaping the future of intelligent, edge-native applications—from self-driving vehicles to industrial automation—making her work essential reading for students and researchers in distributed AI and cyber-physical systems.