Inhee Lee

Hanyang University, University of Michigan–Ann Arbor

Papers

Total Citations

H-Index

About

Inhee Lee is a researcher whose work bridges robotics, personalization, and intelligent sensing technologies. Her key research areas include recommender systems, human-robot interaction, and smart sensor design for autonomous systems. One of her most notable contributions is the development of a **robot recommender system using affection-based episode ontology** (2013, 15 citations), which introduces a hybrid filtering method based on an n-gram affective event model. This work addresses the growing demand for educational robots that can adapt content to individual student motivation, offering a novel framework for personalization through emotional and episodic context. In parallel, Lee has made significant strides in sensing technology with her work on a **smart CMOS image sensor with high signal-to-background ratio and subpixel resolution** (2009, 6 citations). Designed for light-section-based range finding, this sensor enables reliable obstacle detection even under strong ambient light—a critical capability for home service robots and autonomous vehicles. Her contributions reflect a dual focus on enhancing both the perceptual and affective capabilities of intelligent systems. With a career that spans robotics, computer vision, and affective computing, Inhee Lee continues to influence the development of more responsive and context-aware autonomous technologies.