I am a PhD student in Statistics at the University of Connecticut, advised by HaiYing Wang and Jun Yan. My work centers on massive-data subsampling, model averaging, and survival analysis, with a steady interest in building practical statistical software. I expect to graduate in 2027 and am currently on the job market!
I study subsampling methods that preserve statistical efficiency while reducing computational cost. I develop and implement these methods for generalized linear models and survival models, including settings with rare binary outcomes or features.
My background includes accelerated failure time models, additive hazards models, and frequentist model averaging methods for censored outcomes.
I study clinical trial designs with time-to-event outcomes and develop and implement novel statistical methods for these designs.
I am developing a first-authored methodology paper for regression settings with rare binary covariates, including theory clarifying why estimation becomes unstable and a balanced subsampling framework that improves rare-feature representation.
I developed a modular workflow to reconstruct synthetic individual participant data from published Kaplan–Meier curves and summary statistics. The workflow closely reproduced reported survival metrics, recovered additional quantities in simulation studies, and was applied to internal data.
I implemented scalable subsampling methods in R for generalized linear models, rare-feature settings, softmax regression, rare event logistic regression, and quantile regression, with documentation and reproducible examples.
I coauthored work on predictive-modeling-assisted interim analysis for censored time-to-event trials, including covariate-informed prediction for censored participants and evaluation metrics for conditional power accuracy and futility decisions; the manuscript is under revision.
I helped frame and prepare a study of how social determinants of health relate to frailty and accelerated aging among breast cancer survivors. My work focused on retrieving, cleaning, and integrating All of Us EHR, survey, and physical-measurement data for subsequent statistical analysis.
I analyzed fluorescence response data from porphyrinoid sensors using clustering and statistical summaries to support compound differentiation and sensor selection.
I am contributing to a survey project on time series explainability with an emphasis on LLM-enabled semantic explanations, including benchmark curation and related research synthesis; the manuscript is currently under review.
Published and ongoing research.
† Equal contribution.
† Equal contribution.
In Chinese.
In Chinese.
Advisors: HaiYing Wang and Jun Yan.
Advisor: Hui Zhao.
Thesis: Variable Selection and Model Averaging Methods of Accelerated Failure Time Models.
Thesis: Automatic Grading System Based on Convolutional Neural Networks.