Welcome to my website! I’m a 5th-year Ph.D. student in Department of Statistics at Rutgers University. I am very fortunate to have Pierre C. Bellec as my advisor. My research interests span a wide range of areas in Statistics and Machine Learning, including high dimensional statistical inference, dimension reduction, and causal inference. Specifically, 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.
Recent news
[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.