Yiding Wang

Hong Kong University of Science and Technology

Papers

Total Citations

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About

Yiding Wang is a researcher at the forefront of edge-cloud video analytics, with a particular focus on enabling real-time, computation-intensive deep learning applications for robotics. His major contributions center on designing adaptive systems that intelligently compress video data to balance the competing demands of high accuracy, low latency, and limited network bandwidth. By offloading heavy neural network processing to the cloud while maintaining responsiveness at the edge, Wang’s work directly addresses a critical bottleneck in deploying advanced AI in dynamic, resource-constrained environments. His most-cited paper, "Enabling Edge-Cloud Video Analytics for Robotics Applications" (2022), has garnered 61 citations, reflecting its significant impact on both the systems and robotics communities. A related 2021 publication on the same topic has also received 30 citations, underscoring the sustained interest in his approach. Wang’s research is particularly notable for its practical orientation, bridging the gap between theoretical system design and real-world robotic deployment. His work is essential reading for students and researchers exploring efficient video analytics pipelines, edge intelligence, and the future of autonomous systems.