Rate My Professor yu lisha

Professor Lisha Yu is an Assistant Professor of Teaching in the Division of Artificial Intelligence at Lingnan University's School of Data Science. She serves as Programme Director for the Doctor of Artificial Intelligence Studies (DAIS) and as a Centre Fellow at the LEO Dr David P. Chan Institute of Data Science. Yu obtained her BMath and MMath degrees from the University of Waterloo and her PhD from City University of Hong Kong in 2019, with a doctoral thesis titled 'Statistical Methods for System Monitoring and Its Applications' supervised by K. L. Tsui. Following her doctorate, she conducted research at City University of Hong Kong, contributing to studies on personalized health monitoring systems for community-dwelling elderly people in Hong Kong and assessments of elderly functional balance using wearable accelerometers. Her career at Lingnan University includes roles on school-level committees, such as the School of Data Science management board and the Division of Artificial Intelligence committee, as well as membership in the Teaching and Learning Professional Network.

Yu's research focuses on statistical learning, machine learning, quality engineering, and health monitoring and management, with particular emphasis on wearable sensor-based technologies for gait anomaly detection, fall risk assessment, and interventions for older adults. Her publications include 'A data-knowledge driven approach with wearable sensors for intelligent gait disorders identification' (2026, Biomedical Signal Processing and Control), 'A Multimodal Classification Architecture Applied to Gait Anomaly Detection for the Elderly' (2026, Advanced Data Mining and Applications), 'Enhancing 3D Human Pose Reconstruction from Depth Sensors: Uncertainty-Constrained Bayesian Ridge Regression Approach' (2026, IEEE Transactions on Instrumentation and Measurement), 'A Novel Dynamic Latent Variables-Based Framework for Enhancing Freezing of Gait Detection in Parkinson's Disease Patients' (2025), 'Effectiveness of sensor-based interventions in improving gait and balance performance in older adults: systematic review and meta-analysis' (2024, Journal of NeuroEngineering and Rehabilitation), 'A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults' (2022, Sensors), 'Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests' (2021), and 'A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study' (2020). She teaches courses such as Statistics for Data Science and supervises projects in artificial intelligence and business analytics.