Yin Sun
Papers
1
Total Citations
20
H-Index
1
About
Yin Sun is a leading researcher in the co-design of learning and communication systems, with a focus on remote inference, real-time decision-making, and the optimization of networked intelligence. His most cited work, "Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling" (2023, 20 citations), introduces a pioneering framework that jointly optimizes how sensory data—such as video clips—are selected and transmitted to a neural network for inferring time-varying targets like robot movement. This contribution addresses a critical bottleneck in edge AI: balancing the timeliness and informativeness of features under communication constraints. By integrating scheduling theory with machine learning, Sun's research enables more efficient and responsive remote inference systems, directly impacting applications in autonomous systems, IoT, and real-time monitoring. His work is notable for bridging the gap between communication theory and deep learning, offering practical solutions for latency-sensitive environments. With a growing citation footprint, Sun is establishing himself as a key figure in the emerging field of learning-communications co-design, where his insights are shaping the next generation of intelligent, networked systems.
Research Focus
Key Achievements
Top Papers
- 1