Alison Cleary

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About

Alison Cleary is a leading researcher at the intersection of embedded systems and artificial intelligence, with a primary focus on deploying machine learning (ML) on resource-constrained edge devices, particularly small unmanned aerial vehicles (UAVs). Her most cited work, "Machine Learning on Small UAVs" (2020, 11 citations), addresses the critical challenge of moving ML inference from cloud-based, resource-rich environments to the operational edge. In this paper, Cleary and her team at Draper Laboratory detail the concept of operations, design parameters, and real-world constraints encountered when implementing ML workflows directly onboard small drones. This contribution is foundational for enabling real-time, autonomous decision-making in size, weight, and power (SWaP)-limited platforms. Her work directly impacts fields like precision agriculture, disaster response, and defense surveillance, where low-latency, on-board intelligence is essential. Though her citation count is modest, reflecting the niche and applied nature of her research, Cleary’s achievements are notable for bridging the gap between theoretical ML and practical, deployable systems. She is recognized for her rigorous approach to system-level design, ensuring that algorithms are not only accurate but also viable under the strict hardware constraints of small UAVs.