Duowen Liu

Hong Kong University of Science and Technology

Papers

Total Citations

H-Index

About

Duowen Liu is a leading researcher at the intersection of edge computing, cloud systems, and robotics, with a primary focus on enabling real-time, high-accuracy video analytics. His most impactful work, the paper "Enabling Edge-Cloud Video Analytics for Robotics Applications," has accumulated over 90 citations across its 2021 and 2022 versions, establishing him as a key voice in this rapidly evolving field. Liu’s major contribution lies in addressing the fundamental tension between the computational demands of deep learning-based video tasks and the constraints of limited network bandwidth and latency. He has pioneered adaptive data compression strategies for edge-cloud architectures, allowing robotic systems to offload intensive neural network processing to the cloud without sacrificing responsiveness. This work is critical for applications ranging from autonomous navigation to industrial inspection. By systematically balancing accuracy, bandwidth, and delay, Liu’s research provides a practical framework for deploying sophisticated AI in resource-constrained robotic environments. His achievements are particularly notable for bridging the gap between theoretical edge computing models and real-world robotic deployment, making his work essential reading for students and engineers designing next-generation autonomous systems.