My research spans privacy-preserving synthetic data generation, differential privacy, machine learning methods for social science, and election forecasting. Publications appear in venues including ICLR, PNAS, Harvard Data Science Review, and Political Analysis.
Examining what privacy guarantees synthetic data can satisfy even without formal guarantees during synthesizer training.
Examining researcher variability in computational reproduction across 85 independent teams.
Guidelines for deriving meaningful interpretations from regression results with log-transformed dependent variables.
Algorithms for continually releasing differentially private synthetic data from longitudinal data collections.
Demonstrating why hyperparameter tuning and documentation should be standard in ML robustness checks.
A general-purpose method combining bootstrap with differentially private non-parametric distribution estimation.
Multi-lab collaboration revealing how researcher degrees of freedom lead to divergent results from identical data.
Differentially private method combining samples from GAN training to create high-quality synthetic data.
A framework to evaluate the quality of differentially private synthetic data from an applied researcher’s perspective.
Predicting candidate-vote shares at the district level for German Federal Elections.