Biography
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.
Interests
- Recurrent/clustered events
- Mixed effect models
- Accelerometer/Wearable device
- Real-time monitoring using control charts
- Causal inference
Education
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Ph.D. in Biostatistics, 2020 (expected)
University of Michigan
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M.Sc. in Biostatistics, 2016
University of Michigan
