Yiming Luo
Papers
2
Total Citations
15
H-Index
2
About
Yiming Luo is a rising researcher at the intersection of artificial intelligence, human-robot interaction, and affective computing. Their work focuses on two critical frontiers: enabling machines to understand human emotions through multimodal data, and designing socially-aware robots that can navigate complex, real-world social dynamics. Luo’s most cited paper, “Text-guided deep correlation mining and self-learning feature fusion framework for multimodal sentiment analysis” (2025, 8 citations), introduces a novel framework that mines deep correlations between text, audio, and visual cues to improve sentiment recognition—a key step toward more empathetic AI. Equally impactful is their study on robot responses to user bullying (2024, 7 citations), which pioneers the exploration of how robots can de-escalate aggressive behavior through optimized, context-aware replies. This work addresses a pressing gap in human-robot interaction, as robots increasingly serve in public-facing roles. With a growing citation record and research that bridges technical innovation and social responsibility, Yiming Luo is shaping how machines perceive and respond to human emotion—making them not just smarter, but more considerate partners in our daily lives.
Research Focus
Key Achievements
Top Papers
- 1
- 2