Daniel Kang
Papers
1
Total Citations
15
H-Index
1
About
Daniel Kang is a leading researcher at the intersection of cloud robotics, networked systems, and machine learning. His work addresses a critical bottleneck in modern robotics: the computational demands of deep neural networks (DNNs) for tasks like perception, localization, and object detection on resource-constrained platforms such as low-power drones. In his highly cited 2019 paper, "Network Offloading Policies for Cloud Robotics: A Learning-Based Approach," Kang pioneered a learning-driven framework that intelligently decides when to offload computation from a robot to the cloud, balancing latency, accuracy, and network bandwidth. This contribution has garnered 15 citations and laid foundational groundwork for scalable, real-time robotic systems. Kang’s research is notable for its practical impact, enabling cheaper, lighter robots to perform complex tasks without sacrificing performance. His work is essential reading for students and engineers designing next-generation autonomous systems that must operate under severe computational and energy constraints.
Research Focus
Key Achievements
Top Papers
- 1Network Offloading Policies for Cloud Robotics: A Learning-Based Approach15 citations · 2019