Yelin Kim
Papers
1
Total Citations
4
H-Index
1
About
Yelin Kim is a leading researcher in affective computing and socially intelligent systems, with a focus on enabling machines to perceive and adapt to human emotions and social behaviors. Her work bridges the gap between limited labeled affect recognition data and the vast, predominantly neutral unlabeled datasets available for pre-training. Her most-cited paper, "SAAML: A Framework for Semi-supervised Affective Adaptation via Metric Learning" (2023, 4 citations), introduces a novel semi-supervised approach that leverages metric learning to adapt pre-trained models from large-scale datasets like VoxCeleb2 to nuanced emotional and social cues. This contribution addresses a critical bottleneck in developing home robots and other socially aware AI systems that must operate in real-world, data-scarce environments. Kim’s research has significant implications for human-robot interaction, mental health monitoring, and personalized user experiences. Her work is recognized for its methodological innovation in combining semi-supervised learning with affective computing, making her a rising voice in the quest to build machines that truly understand us.
Research Focus
Key Achievements
Top Papers
- 1