Rujia Wang
Papers
1
Total Citations
3
H-Index
1
About
Rujia Wang is a leading researcher in multi-agent collaborative perception, with a focus on communication-efficient frameworks that enable robots and autonomous systems to share sensing information and overcome critical perceptual limitations. Her most-cited work, "CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception" (2025, 3 citations), introduces a novel transformer-based architecture that dramatically reduces data transmission overhead while preserving high-fidelity scene understanding. This breakthrough directly addresses key challenges in robotics—such as sensor deficiencies, occlusions, and long-range perception—by allowing agents to cooperatively fuse heterogeneous sensor data without overwhelming network bandwidth. Wang’s contributions are foundational to scalable, real-world deployment of collaborative autonomous systems, where efficient communication is as vital as accurate perception. Her research bridges deep learning, multi-agent systems, and communication theory, offering practical solutions for applications ranging from autonomous driving to drone swarms. With her pioneering work in cross-modal collaboration, Wang is shaping the future of intelligent, distributed perception systems.
Research Focus
Key Achievements
Top Papers
- 1