Dr. Ji Yeh Choi |
|
|---|---|
| Highest Degree | PhD in Quantitative Psychology McGill University, Montreal, QC, Canada |
| Position | Associate Professor |
| Faculty | School of Management |
| Telephone/Ext | +971 4 7096 876 |
| Location | MGT 123 |
Position Associate Professor
Email JiYeh.Choi@cud.ac.ae
Telephone/Ext +971 4 7096 876
Location MGT 123
Highest Degree PhD in Quantitative Psychology
McGill University, Montreal, QC, Canada
Faculty School of Management
Biography
Ji Yeh Choi (PhD, McGill University) is an Associate Professor in the School of Management at Canadian University Dubai. Previously, she was a faculty member at York University in Toronto (2019–2025) and at the National University of Singapore (2017–2019). With extensive experience in teaching, research, and academic service across Canada, Singapore, and the UAE, she brings internationally recognized expertise in quantitative methods and applied analytics.
Her research focuses on developing interpretable predictive models that bridge statistics and machine learning, with applications in consumer segmentation, behavioural personalization, and operational analytics. She has published 20 peer-reviewed articles and introduced methodological innovations such as neural-network-enhanced regression (NN-ERA), Bayesian mixture regression, and functional clustering. Through this work, she has built a strong profile in Business Analytics and Data Science, combining methodological development with the application of analytical tools to real-world business contexts.
In addition, she enjoys traveling with her family, exploring new cuisines, and spending time with her two children.
Ji Yeh Choi (PhD, McGill University) is an Associate Professor in the School of Management at Canadian University Dubai. Previously, she was a faculty member at York University in Toronto (2019–2025) and at the National University of Singapore (2017–2019). With extensive experience in teaching, research, and academic service across Canada, Singapore, and the UAE, she brings internationally recognized expertise in quantitative methods and applied analytics.
Her research focuses on developing interpretable predictive models that bridge statistics and machine learning, with applications in consumer segmentation, behavioural personalization, and operational analytics. She has published 20 peer-reviewed articles and introduced methodological innovations such as neural-network-enhanced regression (NN-ERA), Bayesian mixture regression, and functional clustering. Through this work, she has built a strong profile in Business Analytics and Data Science, combining methodological development with the application of analytical tools to real-world business contexts.
In addition, she enjoys traveling with her family, exploring new cuisines, and spending time with her two children.