Dr Kei Hang Katie Chan obtained her Bachelor of Information Engineering degree from the University of Hong Kong. She then received her Master of Public Health in Epidemiology and Biostatistics from the University of Southern California. With the Burroughs Wellcome Fund Inter-school Training Program in Metabolic Diseases Fellowship, she attained the Doctor of Philosophy in Epidemiology from the University of California, Los Angeles (UCLA) studying the genetic architecture of metabolic diseases, particularly type 2 diabetes (T2D) and cardiovascular diseases (CVD). After several years of postdoctoral training at UCLA and Brown University, she identified several shared molecular pathways and gene networks between T2D and CVD. Then, she returned to her home town, Hong Kong and joined the Hong Kong Institute of Diabetes and Obesity in the Chinese University Hong Kong (CUHK) as a Research Assistant Professor in 2016. She is also an Adjunct Assistant Professor at the Center for Global Cardiometabolic Health in the Department of Epidemiology in Brown University. In 2017, she was invited to be engaged as Assistant Professor (by courtesy) in the Department of Medicine and Therapeutics in CUHK. While at CUHK, she mainly investigated the genetic determinants of type 2 diabetes and its comorbidities. In 2018, she joined the Departments of Biomedical Sciences and Electronic Engineering in the City University of Hong Kong.
Dr Chan’s research interests include Genetic and Molecular Epidemiology, Systems Biology, Computational Biology and Bioinformatics. Her research focuses on studying the complex network of multifaceted diseases with major global burden by integrating variants in multiple omic levels, biomarkers and environmental data collected in diverse populations, which may deliver a novel preventive approach, diagnoses, and treatment. Below are her current research themes:
- Diseases determinants identification - including but not limited to genetic variants, copy number variations, molecular pathways, gene networks and biomarkers
- Diseases prediction and prevention
- Bioinformatics tools development