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Jooho Kim

Jooho Kim

PhD Student, School of Statistics, University of Minnesota-Twin Cities

I am a first-year PhD student in the School of Statistics at the University of Minnesota-Twin Cities. I received my M.S. in Statistics from Seoul National University, advised by Professor Yei Eun Shin, where my research was supported by a graduate fellowship from the National Research Foundation of Korea. Before that I studied at Korea University, double majoring in Food and Resource Economics and in Statistics.

Research

Research interests

My work so far has been on statistical inference from data that are missing by design. In my master's research I developed a multiple imputation method that preferentially imputes the individuals with greater influence on the target parameter, which reduces the computational burden of imputing multi-dimensional covariates. By integrating two subsamples drawn under different sampling schemes through weight calibration, the method attains relative efficiency comparable to imputing the full cohort.

I am interested in a broad range of statistical problems that help applied researchers draw more rigorous and reliable conclusions. I am early in my PhD and still exploring the directions to pursue.

missing data · data integration · statistical machine learning · high-dimensional inference

Papers

Publications and preprints

  1. Kim, J., Saegusa, T., and Shin, Y. E. (2026+). Scalable and efficient multiple imputation for influence-based supersampled case-cohort studies. arXiv:2511.14692.

    Revision invited, Biometrics.

    Abstract

    In two-phase sampling designs, multiple imputation (MI) is commonly used to impute missing covariates outside the phase-2 sample when estimating hazard ratios in the Cox proportional hazards model. However, standard multiple imputation induces bias when nonlinear terms or interactions are present in the analysis model. Although the substantive-model-compatible fully conditional specification (SMC-FCS) effectively mitigates this bias, it becomes computationally intensive for large cohorts. While the existing supersampling framework, which imputes a randomly chosen subset of individuals outside the phase-2 sample, improves the scalability of SMC-FCS, its reliance on random selection reduces statistical efficiency in log-hazard ratio estimation. We propose an influence-based supersampling (ISS) approach that improves both the scalability of SMC-FCS and the statistical efficiency. By preferentially imputing individuals with greater influence on the target log-hazard ratio, our method achieves efficiency comparable to that of full-cohort imputation while substantially reducing computational cost. Following the supersampling step, estimation is conducted using post-stratification to enable a unified analysis. The proposed method is especially advantageous for estimating hazard ratios with high-dimensional covariates subject to missingness. Extensive simulation studies and a real-data application based on the National Institutes of Health-American Association of Retired Persons (NIH-AARP) Diet and Health Study demonstrate the strong performance of ISS.

    Paper  /  Code

Talks

Contributed talks

Honors

Honors and awards

Teaching

Teaching

Teaching assistant, University of Minnesota-Twin Cities

Teaching assistant, Seoul National University