Welcome to my website! I’m a Stein Fellow in the Department of Statistics at Stanford University. Prior to this, I obtained my Ph.D. in Statistics from Rutgers University, where I was fortunate to be advised by Prof. Pierre C. Bellec. My research interests span a wide range of areas in Statistical Machine Learning. I develop tools for uncertainty quantification of iterative algorithms (e.g. estimating prediction risks and constructing confidence intervals for parameters of interest), to improve the reliability of statistical machine learning methods.
News
[Aug. 2025]: I joined Stanford Department of Statistics as a Stein Fellow.
[Nov. 2024]: The paper Uncertainty quantification for iterative algorithms in linear models with application to early stopping has been invited for a major revision at the Annals of Statistics.
[Sep. 2024]: The paper Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression is accepted to NeurIPS 2024.
[Aug. 2024]: The paper Corrected generalized cross-validation for finite ensembles of penalized estimators is accepted to the Journal of the Royal Statistical Society, Series B (Statistical Methodology).
[Jun. 2024]: Presented a poster at DIMACS Workshop on Modeling Randomness in Neural Network Training.
[Sep. 2023]: The paper Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions is accepted to NeurIPS 2023.
[May 2023]: I am honored to receive the Gold Medal of student poster competition at the Conference on Recent Advances in Statistics and Data Science, with a Celebration of Professors Regina Liu and Cun-Hui Zhang’s Special Birthdays.
[March 2023]: I am honored to receive the IMS Hannan Graduate Student Travel Award.
[Jan. 2023]: I am honored to receive the Travel Award at the 2023 Statistics Annual Winter Workshop titled “Modern Computational Statistics” hosted by the statistics department at the University of Florida.