Papers
1
Total Citations
99
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1
About
Bong Jun Ko is a leading researcher at the intersection of artificial intelligence and civil infrastructure, with a primary focus on deep learning efficiency and edge computing. His most cited work, "Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures" (2019, 99 citations), introduces a groundbreaking method to compress deep convolutional neural networks without sacrificing accuracy. This enables real-time structural health monitoring on resource-constrained edge devices, a critical advancement for smart city applications and infrastructure resilience. Ko’s contributions address the pressing need for scalable, low-latency AI deployment in the field, bridging the gap between high-performance models and practical, on-site assessment. His work has been instrumental in advancing the field of intelligent infrastructure, demonstrating how model pruning can reduce computational costs while maintaining diagnostic precision. With nearly 100 citations on this seminal paper alone, Ko’s research continues to influence both academic inquiry and industrial adoption, making him a pivotal figure in the drive toward autonomous, AI-driven infrastructure management.
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