Linjie Li

Papers

Total Citations

H-Index

About

Linjie Li is a researcher whose work sits at the fascinating intersection of computer vision and social cognition, exploring how machines can learn to perceive people the way humans do. Her primary research focuses on developing AI systems that go beyond basic facial recognition to understand the complex social dimensions of faces—from objective attributes like gender, age, and identity to nuanced subjective judgments such as trustworthiness, attractiveness, and friendliness. In her influential 2017 paper "Learning to see faces like humans," Li pioneered models that bridge the gap between objective facial analysis and human-like social perception, earning 12 citations. Her complementary work "Learning to see people like people" (5 citations) further advances this vision, challenging conventional computer vision approaches that treat faces merely as classification problems. Li’s contributions are particularly notable for their human-centered approach, recognizing that truly intelligent systems must understand both what people look like and how they are perceived socially. Her research has significant implications for human-computer interaction, social robotics, and ethical AI development, making her a distinctive voice in the push toward more socially aware artificial intelligence.