Welcome to Jin-Hong's Homepage

I am a current PhD candidate in Statistics & Machine Learning at Carnegie Mellon University and fortunate to be advised by Professor Kathryn Roeder and Professor Larry Wasserman. I obtained my Master's degree in statistics at the University of Chicago, advised by Professor Lek-Heng Lim. I have also worked with Professor Jingshu Wang. Before my graduate study, I received my bachelor's degree in statistics from the Department of Mathematics, Sun Yat-sen University, China.

My research interests lie at the intersection of statistics, machine learning, and biomedical applications. I am particularly interested in the following main areas:

• Application: single-cell multi-omics

  • mosaic integration and imputation
  • trajectory inference
  • CRISPR perturbation analysis

• Method: causal inference

  • high-dimensional post-imputation / post-integrated inference
  • unmeasured confounder adjustment: model-based and model-free (with auxiliary information) methods
  • statistical networks

• Theory: overparameterized ensemble learning

  • equivalence of implicit regularization by subsampling and explicit ridge regularization
  • cross-validation and ensemble learning in proportional asymptotic regimes