Research
My research lies at the intersection of survey sampling, missing data analysis, and artificial intelligence. I develop rigorous inference methods with a focus on calibration weighting, entropy-based estimation, and high-dimensional settings, and apply these tools to AI problems including transfer learning, generative modeling, and genomic data analysis.
Research Areas
Survey Sampling & Missing Data
Calibration weighting, generalized entropy methods, model-assisted estimation, fractional imputation, propensity score methods, non-probability samples
Transfer Learning
Calibration-based domain adaptation, distributional shift, multi-source data integration, diffusion model-based nonresponse adjustment
AI-Driven Data Analysis
High-dimensional variable selection, knockoff-based FDR control, ensemble methods, RNA-seq and genomic data analysis
Software
GECal — An R package for Generalized Entropy Calibration in survey sampling. Available on CRAN.
calibration — An R package providing an integrated workflow for survey-weight calibration with a modern API, structured solver diagnostics, and extensible solver backends.