Papers
2
Total Citations
156
H-Index
2
About
Seongmin Hong is a leading researcher at the intersection of secure embedded systems and efficient machine learning, whose work addresses critical challenges in both cyber-physical security and deep learning deployment. His most influential contribution, the highly cited "Toward a Secure Drone System" (130 citations), introduced a groundbreaking approach to real-time homomorphic authenticated encryption for drone platforms, directly tackling the urgent security vulnerabilities in wirelessly controlled vehicles and infrastructure. This work has become a foundational reference for securing autonomous systems against cyber threats. Hong further demonstrates his versatility with "FIXAR" (26 citations), a fixed-point deep reinforcement learning platform that pioneers quantization-aware training and adaptive parallelism. This innovation enables DRL agents—typically computationally intensive—to operate efficiently on resource-constrained edge devices, expanding the practical reach of reinforcement learning in robotics and gaming. By bridging the gap between theoretical security protocols and practical hardware constraints, Hong’s research has profound implications for the safe and efficient deployment of autonomous systems, making him a key figure in advancing both secure drone technology and accessible deep reinforcement learning.
Research Focus
Key Achievements
Top Papers
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