Papers
2
Total Citations
7
H-Index
1
About
Hyunsei Lee is a pioneering researcher at the intersection of hyperdimensional computing (HDC) and embodied AI, with a focus on enabling efficient, on-device machine learning for resource-constrained robotic systems. Their major contributions lie in reimagining symbolic learning for real-world sensorimotor control, most notably through the development of ReactHD—a lightweight HDC framework that achieves robust wheeled robot navigation while dramatically reducing energy consumption compared to traditional deep learning approaches. This work, published in 2024, has already garnered 6 citations for its practical demonstration of HDC "in the wild." Lee further advanced the field by introducing the first HDC-based federated learning framework for mobile robots, leveraging synthetic oversampling to address data heterogeneity and privacy concerns in distributed multi-robot systems. This 2025 publication marks a significant step toward scalable, privacy-preserving collaborative learning without the computational overhead of neural networks. By proving that hyperdimensional vectors can rival deep learning in robotic control tasks, Lee is establishing a new paradigm for ultra-low-power autonomous systems, with direct implications for swarm robotics, edge AI, and sustainable machine learning.
Research Focus
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Top Papers
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