Amos Storkey
Papers
4
Total Citations
95
H-Index
4
About
Amos Storkey is a researcher whose work spans machine learning, robotics, and artificial intelligence, with particular expertise in biological movement analysis, neural network optimization, and autonomous systems. His early research made notable contributions to understanding how biological movements are structured, demonstrating through handwriting analysis that motion can be decomposed into compact "motion primitives" — a finding with significant implications for robotic control systems, earning 51 citations. Storkey has also addressed the practical challenge of deploying deep convolutional neural networks on resource-constrained devices, with his work on across-stack CNN optimizations garnering over 40 citations across related publications, helping bridge the gap between powerful AI models and real-world hardware limitations. More recently, his research has turned toward personalizing large language model planners for household robotics, exploring how reinforced self-training can better align AI systems with individual human preferences — a timely contribution as LLMs increasingly enter physical environments. Together, Storkey's body of work reflects a consistent commitment to making intelligent systems both biologically informed and practically deployable, positioning him as a versatile contributor to the intersection of machine learning and real-world robotics applications.
Research Focus
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Top Papers
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