Peer Reviewed Journals

^† corresponding author    * PhD advisee

1. Hwang, Y.^†, Kim, H. J., Chang, W., Hong, C., and MacEachern, S. N. (2025)

   Bayesian model calibration and sensitivity analysis for oscillating biological experiments

   Technometrics, 67, 333-343

2. Huang, Y., Kim, H. J., Huang, C-Y., and Kim, M-O.^† (2025)

   Bayesian random-effects meta-analysis integrating individual participant data and aggregate data

   Journal of the American Statistical Association

3. Kim, H. J.^†, MacEachern, S. N., Kim, Y. M., and Jung, Y. (2025)

   Kernel density estimation with a Markov chain Monte Carlo sample

   Computational Statistics and Data Analysis

4. Chang, W., Hwang, Y., and Kim, H. J.^† (2025)

   Physics-driven dynamic interpolation with application to pollution satellite images

   Spatial Statistics, 69, 100923

5. Lim, Y., Kim, H. J., and Hwang, B. S.^† (in press)

   Nonparametric Bayesian latent class model for longitudinal zero-inflated count data

   Journal of Nonparametric Statistics

6. Ji, E., Ohn, J. H., Jo, H., Park, M-J., Kim, H. J., Shin, C. M.^†, and Ahn, S.^† (2025)

   Evaluating the utility of data integration with synthetic data and statistical matching

   Scientific Reports, 15:19627

7. Lee, S., Chae, S. J., Jang, I-H., Oh, S-C., Kim, S.-M., Lee, S. Y., Kim, J. H., Ko, J., Kim, H. J., Song, I-C., Kim, J. K.^†, and Kim, T-D.^† (2024)

   B7H6 is the predominant activating ligand driving natural killer cell-mediated killing in patients with liquid tumours: evidence from clinical, in silico, in vitro, and in vivo studies

   eBioMedicine, 110, 105459

8. Park, M-J., Kim, H. J., and Kwon, S.^† (2024)

   Disseminating massive frequency tables by masking aggregated cell frequencies

   Journal of the Korean Statistical Society, 53, 328-348

9. Zang, H.*, Kim, H. J.^†, Huang, B., and Szczesniak, R. (2023)

   Bayesian causal inference for observational studies with missingness in covariates and outcomes

   Biometrics, 79, 3624-3636

10. Hong, H., Cortez, M. J., Cheng, Y-Y, Kim, H. J., Choi, B.^†, Josic, K.^†, and Kim, J. K.^† (2023)

    Inferring delays in partially observed gene regulation processes

    Bioinformatics, 39

11. Deng, Q., Nam, J. H., Yilmaz, A. S., Chang, W., Pietrzak, M., Li, L., Kim, H. J.^†, and Chung, D.^† (2023)

    graph-GPA 2.0: Improving multi-disease genetic analysis with integration of functional annotation data

    Frontiers in Genetics, 14:1079198

12. Chen, C., Bin, H., Kouril, M., Liu, J., Kim, H. J., and Sivaganesan, S. (2023)

    An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python

    Frontiers in Computer Science, 5:1183380

13. Wang, Z., Kim, H. J., and Kim, J. K.^† (2023)

    Survey data integration for regression analysis using model calibration

    Survey Methodology, 49, 89-115

14. Allen, C., Chang, Y., Neelon, B., Chang, W., Kim, H. J., Li, Z., Ma, Q., and Chung, D.^† (2022)

    A Bayesian multivariate mixture model for high throughput spatial transcriptomics

    Biometrics, 79, 1775-1787

15. Khatiwada, A.,Wolf, B. J., Yilmaz, A. S., Ramos, P. S., Pietrzak, M., Lawson, A., Hunt, K. J., Kim, H. J., and Chung, D.^† (2022)

    GPA-Tree: Statistical approach for functional-annotation-tree-guided prioritization of GWAS results

    Bioinformatics, 38, 1067-1074

16. Thompson, K. J.^† and Kim, H. J. (2022)

    Incorporating economic conditions in synthetic microdata for business programs

    Journal of Survey Statistics and Methodology, 10, 830–859

17. Hu, J.^†, Drechsler, J., and Kim, H. J. (2022)

    Accuracy gains from privacy amplification through sampling for differential privacy

    Journal of Survey Statistics and Methodology, 10, 688-719

18. Kim, H. J.^†, Drechsler, J., and Thompson, K. J. (2021)

    Synthetic microdata for establishment surveys under informative sampling

    Journal of the Royal Statistical Society, Series A, 184, 255-281

19. Jung, Y.^†, MacEachern, S. N., and Kim, H. J. (2021)

    Modified check loss for efficient estimation via model selection in quantile regression

    Journal of Applied Statistics, 48, 866-886

20. Hwang, Y., Kim, H. J.^†, Chang, W., Yeo, K., and Kim, Y. (2019)

    Bayesian pollution source identification via an inverse physics model

    Computational Statistics and Data Analysis, 134, 76-92

21. Kim, H. J.^†, Lu, B., Nehus, E. J., and Kim, M-O.^† (2019)

    Estimating heterogeneous treatment effects for latent subgroups in observational studies

    Statistics in Medicine, 38, 339–353

22. Liu, X., Chen, A., Caicedo-Casso, A., Cui, G., Du, M., He, Q., Lim, S., Kim, H. J., Hong, C., and Liu, Y.^† (2019)

    FRQ-CK1 interaction determines the period of circadian rhythms in Neurospora

    Nature Communications, 10, 4352

23. Kim, H. J., Yu, Z., Lawson, A., Zhao H., and Chung, D. (2018)

    Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA

    Bioinformatics, 34, 2139-2141

24. Kim, H. J.^†, Reiter, J. P., and Karr, A. F. (2018)

    Simultaneous edit-imputation and disclosure limitation for business establishment data

    Journal of Applied Statistics, 45, 63-82

25. Kortemeier, E., Ramos, P. S., Hunt, K. J., Kim, H. J., Hardiman G., Chung D.^† (2018)

    ShinyGPA: An interactive visualization toolkit for investigating pleiotropic architecture using GWAS datasets

    PLoS ONE, 13, (1): e0190949

26. Chung, D.^†, Kim, H. J., and Zhao, H. (2017)

    graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

    PLoS Computational Biology, 13, (2): e1005388

27. Kim, H. J.^†, Cox, L. H., Karr, A. F., Reiter, J. P., and Wang, Q. (2015)

    Simultaneous edit-imputation for continuous microdata

    Journal of the American Statistical Association, 110, 987-999

28. Kim, H. J.^† and MacEachern, S. N. (2015)

    The generalized multiset sampler

    Journal of Computational and Graphical Statistics, 24, 1134-1154

29. Kim, H. J.^†, Karr, A. F., and Reiter, J. P. (2015)

    Statistical disclosure limitation in the presence of edit rules

    Journal of Official Statistics, 31, 121-138

30. Kim, H. J.^†, Reiter, J. P., Wang, Q., Cox, L. H., and Karr, A. F. (2014)

    Multiple imputation of missing or faulty values under linear constraints

    Journal of Business and Economics Statistics, 32, 375-386

Book Chapters, Technical Reports, or Non-English-Language Journal Articles

31. An, S., Doan, T., Lee, J., Kim, J., Kim, Y. J., Kim, Y., Yoon, C., Jung, S., Kim, D., Kwon, S., Kim, H. J., Ahn, J.^†, and Park, C.^† (2023, written in Korean)

    A comparison of synthetic data approaches using utility and disclosure risk measures

    (유용성과 노출 위험성 지표를 이용한 재현자료 기법 비교 연구)

    Korean Journal of Applied Statistics, 36, 141–166

32. Abowd, J. A., Benedetto, G. L., Garfinkel, S. L., Dahl, S. A., Dajani, A. N., Graham, M., Hawes, M. B., Karwa, V., Kifer, D., Kim, H. J., Leclerc, P., Machanavajjhala, A., Reiter, J. P., Rodriguez, R., Schmutte, I. M., Sexton, W. N., Singer, P. E., and Vilhuber, L. (2020)

    The Modernization of Statistical Disclosure Limitation at the U.S. Census Bureau

    Census Working Papers, U.S. Census Bureau, Washington, DC

33. Thompson, K. J., Kim, H. J., Bassel, N., Bembridge, K., Coleman, C., Freiman, M., Garcia, M., Kaputa, S., Riesz, S., Singer, P., Valentine, E, White, K. T., and Whitehead, D. (2020)

    Final Report: Economic Census Synthetic Data Project Research Team,

    ADEP Working Paper Series ADEP-WP-2020-05, U.S. Census Bureau, Washington, DC

34. Park, M-J. and Kim, H. J.^† (2016, written in Korean)

    Statistical disclosure control for public microdata: present and future

    (마이크로데이터 공표를 위한 통계적 노출제어 방법론 고찰)

    Korean Journal of Applied Statistics, 29, 1041–1059

35. Kim, H. J. and Karr, A. F. (2013)

    The effect of statistical disclosure limitation on parameter estimation for a finite population

    Technical Report 183, National Institute of Statistical Sciences, Durham, NC

36. Bakshi, B. R., Kim, H. J., and Goel, P. K. (2011)

    Using thermodynamics and statistics to improve the quality of life-cycle inventory data Thermodynamics and the Destruction of Resources

    in Thermodynamics and the Destruction of Resources, pp. 235-248, Cambridge University Press

Softwares

• clusterMI (ver 1.2.1) by Vincent Audigier and Hang J. Kim
  

  Allows clustering of incomplete observations by addressing missing values using multiple imputation. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods

• synMicrodata (ver 2.0.0) by Hang J. Kim, Juhee Lee, Young-Min Kim, and Jared Murray
  

  Synthetic microdata generator based on a non-parametric Bayesian model for mixed data type (continuous and categorical variables) where missing values exist

• GGPA (ver 1.16.0) by Dongjun Chung, Hang J. Kim, and Carter Allen
  

  A graphical model for prioritizing GWAS results and investigating pleiotropic architecture, introduced in Chung et al. (2017, PLoS Comput. Biol.) and Kim et al. (2018, Bioinformatics)

• DPImputeCont (ver 1.2.2) by Hang J. Kim
  

  Imputation engine for continuous data using a Dirichlet process Gaussian mixture model, introduced in Kim et al. (2014, JBES)