Yu‐Ling Chang

About

Yu-Ling Chang is a pioneering researcher at the intersection of social robotics, cognitive aging, and human–robot interaction. Her work focuses on developing intelligent robotic systems that can assess, assist, and interact with older adults, particularly those experiencing mild cognitive impairment (MCI) or memory decline. Chang’s major contributions include designing a brain-inspired self-organizing episodic memory model for companion robots like Pepper, enabling them to provide memory assistance through end-to-end systems. She has also pioneered methods for evaluating sustained attention and mind-wandering in older adults using social robots, with her 2021 study on identifying MCI through human–robot interactions garnering 15 citations. Her innovative approach of asynchronously embedding psychological test questions into natural conversations allows for unobtrusive user profiling, while her work on spatially small-scale approach-avoidance behaviors enables robots to infer object preferences without machine learning. With over 40 citations across her most-cited papers, Chang is advancing the use of socially assistive robots as non-invasive, scalable tools for cognitive health monitoring and personalized support in aging populations.

Research Focus

Key Achievements

Top Papers

1. 1
   Identifying Mild Cognitive Impairment by Using Human–Robot Interactions

   15 citations · 2021
2. 2
   A Brain-Inspired Self-Organizing Episodic Memory Model for a Memory Assistance Robot

   9 citations · 2021
3. 3
   Social Robots for Evaluating Attention State in Older Adults

   8 citations · 2021
4. 4
   Asynchronously Embedding Psychological Test Questions into Human–Robot Conversations for User Profiling

   5 citations · 2020
5. 5
   Spatially Small-scale Approach-avoidance Behaviors Allow Learning-free Machine Inference of Object Preferences in Human Minds

   3 citations · 2023

Key Collaborators

Contact & Links