Hang Joon Kim

For current information, please visit
http://homepages.uc.edu/~kim3h4/
 

B.A.: Yonsei University Seoul, Korea, 2002 (Business)

B.A.: Yonsei University Seoul, Korea, 2002 (Applied Statistics)

M.S.: Yonsei University Seoul, Korea, 2006 (Applied Statistics)

Ph.D.: The Ohio State University Columbus, OH, 2012 (Statistics)

Bayesian analysis; Statistical computing; Nonparametric Bayesian modeling
Missing data; Data privacy; Survey sampling
Computer experiment
Statistical genetics

2012 -2015 Postdoctoral Fellow, Duke University; National Institute of Statistical Science (NISS), Durham, NC

2015 -2021 Assistant Professor in Statistics, University of Cincinnati, OH

2018 -2020 ASA/NSF/Census Research Fellow, U.S. Census Bureau, Washington, DC

2019 -To Present Board of Directors, Korean International Statistical Society (KISS),

2021 -To Present Associate Professor in Statistics, University of Cincinnati,

2021 -2022 Faculty Fellow, NCSES Research Ambassadors Program, National Science Foundation (NSF),

2022 -2023 Undergraduate Program Director (Statistics), University of Cincinnati,

Charles Phelps Taft Research Center, Summer Research Fellowship, 2016, $8,000

U.S. Census Bureau, Summer at Census Scholar, 2017, $3,000

Grant: #YA132318AE0011 (extension #YA1333LB19P00000148), ASA/NSF/Census Research Fellowship, 04/30/2018-01/30/2020, Role:Principal Investigator, $170,379

Grant: #US Census Bureau 1333LB19P00000148 Investigators:Kim, Hang Joon 07-31-2019 -01-30-2020 US Census Bureau Hang Kim Census - additional funds to #1014103, COEUS proposal #9741 Role:PI $40,853.00 Completed Type:Grant

Grant: #Sub #60076671 / R01GM122078 01-13-2020 -05-01-2021 National Institute of General Medical Sciences Statistical Models for Genetic Studies, using Network and Integrative Analysis Role:PI $32,997.00 Completed Type:Grant

11-01-2021 -10-31-2024 Patient-Centered Outcomes Research Institute Mixed Data Meta-analysis: Integration of Individual Participant and Aggregate Data Role:PI $109,134 Active Type:Grant

Investigators:Hong, Christian; Kim, Hang Joon; Lim, Sookkyung 09-01-2024 -08-31-2029 National Institute of General Medical Sciences Fundamental properties of circadian rhythms Role:Collaborator 0.00 Hold Level:Federal

Grant: #SPC-1000013593-GR136476 / DMS-2413823 Investigators:Kim, Hang Joon 07-01-2024 -06-30-2027 National Science Foundation Robust and efficient Bayesian inference for misspecified and underspecified models Role:PI 90423.00 Hold Level:Federal

Peer Reviewed Publications

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 (Published Article)

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 (Published Article)

Kim, H. J. and MacEachern, S. N. (2015. ) The generalized multiset sampler .Journal of Computational and Graphical Statistics, , 24 ,1134-1154 (Published Article)

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 (Published Article)

Park, M-J. and Kim, H. J. (2016. ) (Written in Korean), Statistical disclosure control for public microdata: present and future .Korean Journal of Applied Statistics, , 29 ,1041–1059 (Published Article) (Journal Site)

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 (Published Article)

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 (Published Article)

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 (Published Article) (Accepted Manuscript)

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 (Published Article)

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 (Published Article)

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 (Published Article)

Liu, X., Chen, A., Caicedo-Casso, A., Cui, G., Du, M., He, Q., Lim, L., Kim, H. J., Hong, C., and Liu, Y. (2019. ) FRQ-CK1 interaction determines the period of circadian rhythms in Neurospora .Nature Communications, , 10 ,4352 (Published Article)

Jung, Y., MacEachern, S. N., and Kim, H. J. (2020. ) Modified check loss for efficient estimation via model selection in quantile regression .Journal of Applied Statistics, , 48 ,866-886 (Published Article)

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 (Published Article)

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 (Published Article)

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 (Published Article)

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 (Published Article)

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 (Published Article)

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 ,(Published Article)

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 ,(Published Article)

Wang, Z., Kim, H. J., and Kim, H. J. (2023. ) Survey data integration for regression analysis using model calibration .Survey Methodology, , 49 ,89-115 (Published Article)

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 (Published Article)

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 (Published Article)

Other Publications

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. ) A comparison of synthetic data approaches using utility and disclosure risk measures (written in Korean) .36, 141–166 (Published Article)

Park, M-J. and Kim, H. J.* (2016. ) Statistical disclosure control for public microdata: present and future (written in Korean) .29 ,1041–1059 (Published Article)

Book Chapter

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 .(pp. 235-248).Cambridge University Press

Technical Reports

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, (Report)

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. (2020. ) Final Report: Economic Census Synthetic Data Project Research Team .ADEP Working Paper Series ADEP-WP-2020-05, U.S. Census Bureau, Washington, DC, (Report)

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,