I’m a Ph.D. candidate in Biostatistics at the University of Michigan. My thesis advisors are Prof. Douglas E. Schaubel and Prof. Peter Xuekun Song.
My research topics include developing novel methods and their software for analyzing clustered event data, like alternating recurrent events, competing risks, and events with hierarchical structures.
Moreover, as a research assistant for the University of Michigan Kidney Epidemiology and Cost Center (KECC), I am working on monitoring charts to track performance of dialysis facilities according to the mortality, readmission and hospitalizations.
I am also passionate about recent progresses in machine learning methods and causal inference, and their increasing use in event data analysis.
Ph.D. in Biostatistics, 2020 (expected)
University of Michigan
M.Sc. in Biostatistics, 2016
University of Michigan
Worked on Weighted log-rank test in interim analysis (IA):
Developed an R package IAfrac
for sample size calculation, information fraction estimation, and correlation matrix of the MaxCombo statistic in IA