Where I learned my skills...
Before I joined in the City of Hope, I earned my Master of Science in biostatistics from the University of Washington (2022),
where I had the privilege of being advised by Jim Hughes and
Helen Y. Chu.
During my time there, I actively participated in numerous research focused on combating the COVID-19 pandemic, spanning from
epidemiological studies to molecular investigations.
Prior to my graduate studies, I completed my Bachelor of Science in statistics at the University of California, Los Angeles (2020). At UCLA,
I developed a keen interest in applying advanced machine learning models to gain insights into the behavior of concrete and cement, aiming to mitigate carbon emissions.
I was fortunate to receive guidance from Mathieu Bauchy and
Hongquan Xu, who supported me in exploring this fascinating area of research.
What I am doing now...
I worked as a biostatistician at the City of Hope, focusing primarily on research related to women's cancers such as gynecologic cancer,
breast cancer, and colorectal cancer. My goal was to investigate the disparities in cancer behavior based on various social determinants,
such as race/ethnicity and insurance, as well as biological and molecular information, including genomics, proteomics, and cytokines.
Additionally, I evaluated programs/trials aimed at translating scientific knowledge into community practice to reduce and eliminate inequalities
in cancer outcomes. In my daily work, I frequently addressed questions like:
- Are there disparities in cervical cancer survival rates among different race/ethnicity groups?
- Which mRNA expressions are highly associated with cancer behavior, and could they serve as new biomarkers?
- Does the cancer intervention effectively reduce patients' burdens and improve diagnosis?
Similar cancer-related problems often arose in my work. Regardless of the specific question, statistical analysis played a crucial role in
providing evidence to answer scientific inquiries. I employed various statistical methods, such as survival and longitudinal models,
machine learning, experimental designs, and many other statistical models, integrating them to formulate effective solution pathways for each scientific question.
In addition to my cancer-related research, I also conduct statistical analysis in computational neuroscience to explore the relationship
between neuro signals and animal behaviors.
What I am interested…
My research journey began with the application of variable importance, such as permutation importance and Shapley values, to investigate
the impact of chemical compositions on the strength of concrete/cement. This experience sparked my interest in exploring variable importance for
both semi-parametric and non-parametric models. As I delved into high-dimensional data from respiratory infectious diseases and cancer studies,
which included RNA-seq, proteomics, and cytokine data, I became fascinated with understanding how variable importance can be incorporated into statistical
models for such data types. Given the longitudinal nature and survival-related aspects of clinical and biomedical studies, I developed a specific
interest in utilizing longitudinal and survival models to effectively handle high-dimensional data in practical scenarios. As I immersed myself in
public health studies, I regularly encountered data analysis related to population science and epidemiology. I was intrigued by the prospect of
adapting statistical models to accommodate data from diverse study designs and answer scientific inquiries. Regardless of the aforementioned research
questions, I strongly believe that data visualization plays an integral role in explanatory data analysis. As a biostatistician, I am consistently passionate
about enhancing data visualization techniques to suit various types of data.
- Theory: variable importance, longitudinal and survival analysis, high-dimensional statistics, non-parametric and semi-parametric regression, time-varying models, causal inferece in observational studies
- Application: biomarker discovery, infectious disease modeling, health disparity, observational and epidemiological studies, genomics data analysis, cancer, neuronal data analysis, data visualization
Additionally, I have been practicing Chinese calligraphy for 19 years and continue to cherish this art form : )