Currently working as a Post-Doctoral Scholar at Stanford University in the Department of Biomedical Informatics with Nigam Shah, MBBS, PhD.
I received my PhD from the Department of Population Health Sciences with an emphasis in Biostatistics at the University of Utah in 2020.. My primary research interest is on the development of causal inference methods and its intersection with machine learning approaches.
A brief autobiography
I received my undergraduate degree in nursing from Liaoning Medical University in China. Given my background in medicine and my passion for working on math-related problems, I decided to pursue a Master’s degree in Biostatistics at the University of Utah. While I had quite a bit of “catch-up” work during Master’s program, it solidified my interest in Biostatistics as a career path. I then applied to and was accepted to the PhD program at the University of Utah for further training.
Describe your experience in the program
The Biostatistics track in the Department of Population Health Science trained me to be an applied biostatistician by emphasizing how to appropriately analyze data from clinical studies and use data to provide evidence-based insights into modern medicine. As a student, I not only learned from my classes but also from working with senior statisticians, epidemiologists, and clinicians. The practical experience helped to strengthen the theoretical knowledge learned in classes. The program also supported me to attend conferences, which helped me gain exposure to new ideas and build networks.
What classes did you take?
Linear Models, Survival Analysis, Longitudinal Data Analysis, Categorical Data Analysis, Advanced Research Design, Epidemiology, Modern Causal Inference (Applied & Theory), Machine Learning, Analysis of Secondary Data, Multilevel Data Analysis, and Grant Writing.
Describe some of your research experiences
During my Master's training, my research focused on evaluating the impact of different interventions on a women’s ability to conceive. During my doctoral training, I worked in the Study Design and Biostatistics Center (SDBC) and gained valuable experience in statistical consulting. I also collaborated with researchers from Veterans Affairs (VA) and the U on different applied projects. With VA researchers, we conducted analyses to identify the best time to initiate treatment among patients with rheumatoid arthritis and with rheumatoid arthritis. With U researchers, we assessed heterogeneity of treatment effects, built risk prediction models, and evaluated optimal treatment strategy for participants who enrolled in SPRINT study. I also worked with my mentors on several methodological projects, including developing methods for estimating the optimal individualized treatment rule from a cost-effectiveness perspective, and assessing the performance of several machine learning approaches for identifying heterogeneous treatment effects. In total, I published 18 papers during my training at Utah.
What did you enjoy most about the program?
Seminars! Regular seminars were held by the Department of Population Health Sciences, the Study Design and Biostatistics Center, and the Huntsman Cancer Institute. By attending these seminars, I learned about new research topics and methods from speakers at various universities and from different fields. The department provided opportunities to interact with speakers, which I found very motivating.
What is your next step and how you feel the program prepared you for this?
My next step is a postdoctoral fellowship with the biomedical informatics team at Stanford. This is a large team consisting of researchers with various specialties, and I hope to learn as much as I can from statisticians, informatics professionals, computer scientists, and clinicians. The PhD program prepared me well for working and communicating with researchers from different fields as well as working with electronic health records and programming with Python.
What advice you have for future students?
I consider the seminars provided by the department and other groups as a great resource; I recommend students attend as many as possible. The topics discussed will connect the concepts learned in classes; they may also help trigger thesis ideas. In addition, students should take advantage of the variety of hands-on workshops available at the U.