Cheng-Yi Tang
Papers
1
Total Citations
7
H-Index
1
About
Cheng-Yi Tang is a rising scholar in human-robot interaction (HRI), whose work leverages machine learning to map and predict research trends in this rapidly evolving field. His most-cited study, “A Machine Learning Approach to Model HRI Research Trends in 2010~2021” (2022, 7 citations), introduces a novel ML-driven methodology that applies topic modeling to a large corpus of HRI literature. By automatically extracting and analyzing dominant research factors, Tang’s model identifies five critical topics—including handover dynamics—that have shaped HRI over the past decade. This contribution provides a data-driven, systematic framework for understanding how the field has evolved, offering researchers a powerful tool to identify emerging areas and gaps. Tang’s work stands out for its interdisciplinary approach, merging computational techniques with social robotics to reveal patterns that manual reviews might miss. Though early in his career, his innovative use of machine learning to synthesize HRI trends marks a significant step toward more efficient, evidence-based research planning. His findings not only inform current HRI studies but also lay groundwork for future predictive models, making him a promising voice in the quest to design more intuitive and effective human-robot systems.
Research Focus
Key Achievements
Top Papers
- 1A Machine Learning Approach to Model HRI Research Trends in 2010~20217 citations · 2022