Research interests

Semiparametric theory

for developing efficient inference methods in models with nonparametric components

for constructing valid inference methods based on partially observed data

for enabling reliable inference under distributional differences between source and target data

for designing sampling strategies that minimize loss or maximize efficiency under resource constraints

* ⭢ corresponding author; ⁺ ⭢ lab member; ⁼ ⭢ co-first author

2026+
Surrogate-assisted optimal sampling for risk prediction under measurement constraints
Sunhyun Park⁺, Seong-ho Lee*
submitted
2026
DOD: Detection of outliers in high dimensional data with distance of distances
Seong-ho Lee, Yongho Jeon*
Statistics and Computing
2026
Efficient inference under label shift in unsupervised domain adaptation
Seong-ho Lee, Yanyuan Ma, Jiwei Zhao*
Journal of Machine Learning Research
2025
SPARCC: Semi-parametric robust estimation in a right-censored covariate model
Seong-ho Lee, Brian Richardson⁼, Yanyuan Ma, Karen Marder, Tanya Garcia*
Journal of the American Statistical Association: Theory and Methods
2024
Doubly flexible estimation under label shift
Seong-ho Lee, Yanyuan Ma, Jiwei Zhao*
Journal of the American Statistical Association: Theory and Methods
2023
Semiparametric approach to estimation of marginal mean effects and marginal quantile effects
Seong-ho Lee*, Yanyuan Ma, Elvezio Ronchetti
Journal of Econometrics
2023
Optimal sampling for positive only electronic health record data
Seong-ho Lee*, Yanyuan Ma, Ying Wei, Jinbo Chen
Biometrics
2022
Covariate balancing for causal inference on categorical and continuous treatments
Seong-ho Lee, Yanyuan Ma, Xavier de Luna*
Econometrics and Statistics