Ruining Yang

University of Colorado Denver, Linyi University

Papers

Total Citations

H-Index

About

Ruining Yang is a pioneering researcher at the intersection of mental health technology and intelligent robotics. Her primary research areas include federated learning applications in healthcare robotics, computational verb theory for machine vision, and the development of therapeutic robotic systems. Yang’s most significant contribution is the creation of the Depression Treatment Robot (DTbot), a groundbreaking application of federated learning that addresses one of psychiatry’s most complex challenges. Her 2021 paper on this subject, which has garnered 16 citations, proposes a novel framework where multiple DTbots collaboratively learn from distributed patient data without compromising privacy, offering a scalable, personalized approach to depression therapy. Earlier, Yang demonstrated her innovative thinking with her 2009 work on colour tag design for robot soccer, where she applied computational verb theory to enhance vision system accuracy—a foundational contribution that has earned 3 citations and influenced subsequent robotics competitions. Her work bridges the gap between advanced machine learning and practical mental health interventions, positioning her as a key figure in the emerging field of therapeutic robotics. Yang’s research continues to inspire new directions in privacy-preserving healthcare AI and human-robot interaction.