ORCID iD: 0000-0003-0061-0012
Scopus Author ID: 30067965500

Metrics for Academic and Research Impact

PhD (Duke University)

Dr Yuan received his Bachelor of Science degree in Electrical Engineering from National Central University in Taiwan in 1999. He received his Master of Science in Biomedical Engineering from National Taiwan University in 2001 working on Artificial Intelligence with a focus on Medical Natural Language Processing. He received his second Master degree in Microbiology (Immunology Division) from National Taiwan University under the guidance with Prof. Yuan-Tsong Chen at Academia Sinica. His thesis work focused on the Pharmacogenetic study of Warfarin responsiveness. In 2007, Dr Yuan pursue his PhD degree in Computational Biology & Bioinformatics in the Lab of Katia Koelle at Duke University focusing on mathematical modelling of influenza antigenic drift. After the completion of the graduate school in 2013, Dr Yuan moved to London to study the impact of herd immunity on influenza outbreak with Prof. Steven Riley at Imperial College London. Dr Yuan joined CityU as an Assistant Professor in January 2018.

Our group seeks a creative and analytically-minded research assistant with strong quantitative skills and a background in biomedical sciences or related fields to support our modeling and analytics group. Responsibilities include assembling and curating data sets, building mathematical models using R or matlab (knowing python or C will also be great) packages, performing statistical analysis, performing literature reviews, and writing scientific publications and government reports. 

• B.A., B.S. or M.Sc in Biostatistics, Public Health, Infectious Disease Ecology, Statistics or Data Science, Mathematics, Computer Sciences, or related field.
• Strong quantitative, statistical, and computational skills 
• Demonstrated creativity in data analysis 
• Demonstrated ability to self-teach new software tools and statistical methods 
• Knowledge of statistical or computer software, such as R, matlab or C 

1. Risk assessment of current COVID-19 by modelling the impacts of border controls, vaccination and other public health measures.
2. Exploring the interplay between virus evolution and host immunity dynamics in the population, including how binding avidity adaptation limits disease transmission and diversity. 
3. Predicting virus fitness and traits using AI with a focus on receptor binding affinity.
4. Forecasting of Dengue disease transmission and mathematical modelling of Mosquito lifecycle.
5. Translation of medical discoveries into policies in Epidemiology and public health. For example, to evaluate and predict vaccination outcomes in a population considering vaccine waning.