Xinran Wang

Papers

Total Citations

H-Index

About

Xinran Wang is a researcher working at the intersection of artificial intelligence, multi-agent systems, and robotics. Their work spans collaborative machine learning frameworks, autonomous navigation, and intelligent robotic environments, reflecting a broad yet cohesive research vision centered on making AI systems more capable and cooperative in real-world settings. Wang's most influential contribution is the development of "Assisted Learning" (2020), a pioneering framework addressing how multiple organizations can collaborate on AI tasks without compromising proprietary information — a critical challenge in modern federated and privacy-sensitive AI deployments. This work has garnered 16 citations, establishing Wang as a meaningful voice in the growing field of collaborative and privacy-preserving machine learning. Beyond multi-organizational learning, Wang has contributed to the foundations of intelligent robotic systems, including a 2012 study on intelligent space technology for home service robots and a 2019 investigation into deep reinforcement learning for mobile robot obstacle avoidance. Together, these works demonstrate a sustained commitment to bridging theoretical AI with practical robotics applications. Wang's research trajectory suggests a researcher thoughtfully navigating some of AI's most pressing challenges: enabling machines to learn collaboratively, navigate autonomously, and operate intelligently in dynamic human environments.