Research Interests

I develop and apply statistical and machine learning methods for reliable analysis of observational health data. My applied work has spanned electronic health records (EHR) data, mobile device data, and large-scale biobank data. My methodological work is inspired by my decade of experience working with EHR data and focuses on statistical challenges arising from missing data, measurement error, bias and fairness, data heterogeneity, and the growing use of AI-generated and synthetic data.   

A full list of my publications can be found on Google Scholar. Some recent representative works are highlighted below. 

* indicates a trainee.

2026

McCaw, Z.R., Ocampo, A., Giudice, E., *Song, F. and Gronsbell, J., 2026. Pseudo-value Based Mean Cumulative Count Regression. arXiv preprint arXiv:2606.24024.

*Gao, J. and Gronsbell, J., 2026. Reliable fairness auditing with semi-supervised inference. arXiv preprint arXiv:2505.12181. Accepted at Biometrics.

Gronsbell, J., McCaw, Z.R., Nogues, I.E., *Kong, X., Cai, T., Tian, L. and Wei, L.J., 2026. Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events. Biometrics, 82(2), p.ujag053.

2025

Gronsbell, J., Panickan, V.A., Zhou, D., Lin, C., Charlon, T., Hong, C., Xiong, X., Wang, L., Gao, J., Zhou, S. and Tian, Y., 2025. A Common Pipeline for Harmonizing Electronic Health Record Data for Translational Research. arXiv preprint arXiv:2509.08553.

*Gao, J., *Chou, B., McCaw, Z.R., Thurston, H., Varghese, P., Hong, C. and Gronsbell, J., 2025. What is fair? Defining fairness in machine learning for health. Statistics in Medicine, 44(20-22), p.e70234.

Gronsbell, J., Thurston, H., *Dong, L., Ferguson, V., *Chaudhury, D.S., O’Neill, B., *Sha, K.S. and Bonneville, R., 2025. When algorithms infer gender: revisiting computational phenotyping with electronic health records data. Biology of sex Differences, 17(1), p.16.

2024

Gronsbell, J., *Gao, J., McCaw, Z.R., *Shi, Y. and Cheng, D., 2024. Another look at statistical inference with machine learning-imputed data. arXiv preprint arXiv:2411.19908.

McCaw, Z.R., *Gao, J., Lin, X. and Gronsbell, J., 2024. Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks. Nature genetics, 56(7), pp.1527-1536.

*Gao, J., Bonzel, C.L., Hong, C., Varghese, P., *Zakir, K. and Gronsbell, J., 2024. Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms. Journal of the American Medical Informatics Association, 31(3), pp.640-650.