Research Interests

• Large-scale multivariate statistical learning; integrative statistical learning
• Dimension reduction and variable selection
• Statistical machine learning
• Statistical computing and optimization
• Population health and healthcare analytics
• Ecological and environmental statistics

Grants

• Integrative learning of fluorescence fluctuations in perovskite quantum dots using a data science assisted single-particle approach. National Science Foundation (CHE-2203854). Co-PI (with Dr. Jing Zhao); 09/01/2022–08/31/2025.
• Developing suicide risk algorithms for diverse clinical settings using data fusion. National Institutes of Health (R01-MH124740). Principal Investigator (with Dr. Robert Aseltine and Dr. Fei Wang); 09/16/2020–06/30/2024.
• Improving suicide prediction using NLP-derived social determinants of health. National Institutes of Health (R01-MH125027). Principal Investigator on sub-award (PI: Dr. Hong Yu); 09/01/2020–06/30/2024.
• Improving the identification and management of suicide risk among patients using prescription opioids (HEAL Supplement). National Institutes of Health (R01-MH112148-03S1). Principal Investigator on sub-award (PI: Dr. Robert Aseltine); 09/18/2020–06/30/2021.
• Reciprocal modulation of the microbiome and cellular senescence in metabolic dysfunction. National Institutes of Health (R01-AG068860). Principal Investigator on sub-award (PI: Dr. Yanjiao Zhou); 09/10/2020–05/31/2025.
• Comprehensive heterogeneous response regression from complex data. National Science Foundation (IIS-1718798). Principal Investigator; 09/01/2017– 08/31/2020.
• Improving the identification of patients at risk of suicide. National Institutes of Health (R01-MH112148). Principal Investigator on sub-award (PI: Dr. Robert Aseltine);  07/01/2017–06/30/2020.
• Integrative multivariate analysis with multi-view data. National Science Foundation (DMS-1613295). Principal Investigator; 09/01/2016–08/31/2019.
• An integrative statistics-guided image-based multi-scale lung model. U.S. National Institutes of Health (U01-HL114494). Principal Investigator on sub-award (PI: Dr. Ching-Long Lin); 08/01/2013– 05/31/2018.

Selected Publications

  Methodology & Theory

• Chen, K., Dong, R., Xu, W., and Zheng, Z. (2022) Fast stagewise sparse factor regression. Journal of Machine Learning Research, 23(271):1–45.
• Li, G., Li, Y., and Chen, K. (2022) It’s all relative: regression analysis with compositional predictors. Biometrics. Accepted.
• Li, Y., Li, G., and Chen, K. (2022) Principal amalgamation analysis for microbiome data. Genes, 13(7):1139.
• Cui, S., Liang, J., Pan, W., Chen, K., Zhang, C., and Wang, F. (2022) Collaboration equilibrium in federated learning. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM.
• Liu, X., Cong, X., Li, G., Mass, K., and Chen, K. (2022) Multivariate log-contrast regression with sub-compositional predictors: testing the associations between preterm infant’s gut microbiomes and neurobehavioral outcomes. Statistics in Medicine, 41(3):580–594.
• Liu, X., Ma, S., and Chen, K. (2021) Multivariate functional regression via a nested reduced-rank regularization. Journal of Computational & Graphical Statistics, 31(1):231–240.
• Li, Y., Yu, C., Zhao, Y. Aseltine, R., Yao, W., Chen, K. (2021) Pursuing sources of heterogeneity in modeling clustered population. Biometrics, 78(2):716–729.
• Mishra, A., Chen, Y., Dey, D. K., and Chen, K. (2021) Generalized co-sparse factor regression. Computational Statistics & Data Analysis, 157:107–127.
• Vaughan, G., Aseltine, R., Chen, K., Yan, J. (2020) Efficient interaction selection via stagewise generalized estimation equations. Statistics in Medicine, 39(22):2855–2868.
• Sun, Z., Xu, W., Cong, X., Li, G., Chen, K. (2020) Log-contrast regression with functional compositional predictors: Linking preterm infant’s gut microbiome trajectories in early postnatal period to neurobehavioral outcome. Annals of Applied Statistics, 14(3):1535–1556. (2020 John van Ryzin Award).

• Wang, W., Aseltine, R., Chen, K., Yan, J.(2020) Integrative survival analysis with uncertain event times in application to a suicide risk study. Annals of Applied Statistics, 14(1):51–73.

• Uematsu, Y., Fan, Y., Chen, K., Lv, J., Lin, W. (2019) SOFAR: large-scale association network learning. IEEE Transactions on Information Theory, 65(8):4924–4939.

• Li, G., Liu, X., Chen, K. (2019) Integrative multi-view reduced-rank regression: Bridging group-sparse and low-rank models. Biometrics, 75(2):593–602.

• He, L., Chen, K., Xu, W., Zhou, J., Wang, F. (2018) Boosted sparse and low-rank tensor regression. Advances in Neural Information Processing Systems (NeurIPS) 31, 1017–1026.

• Liang, J., Chen, K., Lin, M., Zhang, C., Wang, F. (2018) Robust finite mixture regression for heterogeneous targets. Data Mining and Knowledge Discovery, 32(6):1509–1560.

• Luo, C., Liang, J., Li, G., Wang, F., Dey, D., Chen, K. (2018) Leveraging mixed and incomplete outcomes via a generalized reduced rank regression. Journal of Multivariate Analysis, 167:378–394.

• Chen, K., Mishra, N., Smyth, J., Bar, H., Schifano, E., Kuo, L., Chen, M.-H. (2018) A tailored multivariate mixture model for detecting proteins of concordant change in the pathogenesis of Necrotic Enteritis. Journal of the American Statistical Association, 113:546–559.

• Mishra, A., Dey, D., Chen, K. (2017) Sequential co-sparse factor regression. Journal of Computational and Graphical Statistics, 26(4):814–825.

• She, Y., Chen, K. (2017) Robust reduced-rank regression. Biometrika, 104(3):633–647.

• Vaughan, G., Aseltine, R., Chen, K., Yan, J. (2017) Stagewise generalized estimation equations with grouped variables. Biometrics, 73:1332–1342.

• Goh, G., Dey, D., Chen, K. (2017) Bayesian sparse reduced-rank regression. Journal of Multivariate Analysis, 157:14–28.

• Lachos, V., Morenno, E., Chen, K., Cabral, C. (2017) Finite mixture modeling of censored data using the multivariate student-t distribution. Journal of Multivariate Analysis, 159, 151-167.

• Chen, K., Ma, Y. (2017) Analysis of double single index models. Scandinavian Journal of Statistics. 44(1), 1-20.

• Dong, H., Chen, K., Linderoth, J. (2016) Regularization vs. relaxation: A conic optimization perspective of statistical variable selection. arXiv:1510.06083.

• Chen, K., Hoffman, E., Seetharaman, I., Jiao, F., Lin, C.L., Chan, K.-S. (2016) Linking lung airway structure to pulmonary function via composite bridge regression. Annals of Applied Statistics, 10(4), 1880-1906.

• Chen, K. (2016) Model diagnostics in reduced-rank estimation. Statistics and Its Interface, 9(4), 469-484.

• Luo, C., Liu, J., Dey, D., Chen, K. (2016) Canonical variate regression. Biostatistics, 17(3), 468-483.

• Mukherjee, A., Chen, K., Wang, N., Zhu, J. (2015) On the degrees of freedom of reduced-rank estimators in multivariate regression. Biometrika, 102(2), 457-477.

• Chen, K., Chan, K.-S., Stenseth N. R. (2014). Source-sink reconstruction through regularized multi-component regression analysis --with application to assessing whether North Sea cod larvae contributed to local fjord cod in Skagerrak. Journal of the American Statistical Association, 109(506), 560-573.

• Chen, K., Dong H., Chan, K.-S. (2013). Reduced rank regression via adaptive nuclear norm penalization. Biometrika, 100(4), 901-920.

• Chen, K., Chan, K.-S., Stenseth, N. R. (2012). Reduced rank stochastic regression with a sparse singular value decomposition. Journal of the Royal Statistical Society (Series B), 74(2), 203-221.

• Chen, K., Chan, K.-S. (2011). Subset ARMA model selection via the adaptive Lasso. Statistics and Its Interface, 4, 197-205.

  Application & Case Study

• Xu, W., Chang, S., Li, Y., Doshi, R., Chen, K., Wang, F., and Aseltine, R. (2022) Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data. Journal of the American Medical Informatics Association, 29(3):500–511. (Featured article).
• Aseltine, R., Chen, K., Wang, F., and Jin, J. (2022) Harnessing big data in health care: Challenges in enhancing the clinical utility of patient data for suicide prevention. Connecticut Medicine, 86(1):61–66.
• Sun, Y., Wang, Y., Zhu, H., Jin, N., Mohammad, A., Biyikli, N., Chen, O., Chen, K., and Zhao, J. (2022) Excitation wavelength-dependent photoluminescence decay of single quantum dots near plasmonic gold nanoparticles. Journal of Chemical Physics, 156:154701.
• Wang, J., Tang, K., Feng, K., Lin, X., Lv, W., Chen, K., and Wang, F. (2021) Impact of temperature and relative humidity on the transmission of COVID-19: a modeling study in China and the United States. BMJ Open, 11:e043863.
• Chang, S., Aseltine, R., Riddhi†, D., Chen, K., Rogers, S., and Wang, F. (2020) Machine learning for suicide risk prediction in children and adolescents with electronic health records. Translational Psychiatry, 10, 413.
• Doshi, R., Chen, K., Wang, F., Schwartz, H., Herzog, A., Aseltine, R. (2020) Identifying risk factors for mortality among patients previously hospitalized for a suicide attempt. Scientific Reports, 10:15223.
• Li, X., Dou, F., Guo, J., Velarca, M. V., Chen, K., Gentry, T., McNear, D. (2020) Soil microbial community responses to nitrogen application in organic and conventional rice (Oryza Sativa L.) production. Soil Science Society of America Journal. In press.
• Liu, Y., Huang, J., Urbanowicz, R. J., Chen, K., Manduchi, E., Greene, C. S., Scheet, P., Moore, J. H., Chen, Y. (2020) Embracing heterogeneity for finding genetic interactions in large- scale research consortia. Genetic Epidemiology, 44(1):52–66.
• Doshi, R., Aseltine, R., Wang, F., Schwartz, H., Rogers, S., Chen, K. (2018) Illustrating the role of health information exchange in a learning health system: Improving the identification and management of suicide risk. Connecticut Medicine, 82(6):327–333.
• Chen, K., Aseltine, R. (2017) Using hospitalization and mortality data to target suicide prevention activities. Journal of Adolescent Health, (61):192-197.
• Choi, S., Hoffman, E. A., Wenzel, S. E., Castro, M., Fain, S., Jarjour, N., Schiebler, M. L., Chen, K., and Lin, C.-L. (2017) Quantitative computed tomography imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes. Journal of Allergy and Clinical Immunology, 140(3):690–700.

• Chen, Y., Chen, K., and Kalichman, S. C. (2017) Barriers to HIV medication adherence in the context of regimen simplification. Annals of Behavioral Medicine, 51(1):67–78.

• Dou, F., Soriano, J., Tabien, R., and Chen, K. (2016) Soil texture and cultivar effects on rice (Oryza sativa, L.) grain yield, yield components and water productivity in three water regimes. PLoS ONE, 11(3):e0150549.

• Choi, S., Hoffman, E.A., Wenzel, S.E., Castro, M., Fain, S., Jarjour, N., Schiebler, M.L., Chen, K., Lin, C.-L. (2015) Quantitative assessment of multiscale structural and functional alterations in asthmatic populations. Journal of Applied Physiology, 118(10), 1286-1298.

• Chen, K., Ciannelli, L., Decker, M.B., Ladd, C., Cheng, W., Zhou, Z., Chan, K.S. (2014) Reconstructing source-sink dynamics in a population with a pelagic dispersal phase. PLoS ONE, 9(5): e95316.

• Chen, K., Chan, K.-S., Bailey, K., Aydin, K., and Ciannelli, L. (2012) A probabilistic cellular automata approach for predator-prey interactions of arrowtooth flounder (Atheresthes stomias) and walleye pollock (Theragra chalcogramma) in the eastern Bering Sea. Canadian Journal of Fisheries and Aquatic Sciences, 69(2):259–272.