Xin-meng Wang
Papers
2
Total Citations
12
H-Index
2
About
Xin-meng Wang is an emerging researcher at the intersection of human-robot interaction, multimodal AI, and engineering project automation. Their work centers on two key areas: enhancing social intelligence in machines through non-verbal cue understanding, and streamlining complex engineering workflows with generative AI. Wang’s most cited paper, “Joint Attention Estimation during Multi-party Facilitation Using Multi-Modal Fusion” (2024, 7 citations), introduces a novel framework that enables AI systems to interpret visual attention—such as gaze direction and eye contact—during group interactions, a critical step for making robots more natural collaborators in human settings. Complementing this, their second highly cited work, “LLM-Project: Automated Engineering Task Planning via Generative AI and WBS Integration” (2024, 5 citations), demonstrates a practical application of large language models by integrating them with Work Breakdown Structure methodology to automate task decomposition and planning. Though early in their career, Wang’s dual focus on social perception and structured automation signals a promising trajectory toward building robots that can both understand human social dynamics and execute complex, multi-step tasks. Their work is already informing next-generation collaborative systems in manufacturing, healthcare, and service robotics.
Research Focus
Key Achievements
Top Papers
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