* = cofirst author
† = alphabetical order
Preprints

Deshpande, S. K., Bai, R., Balocchi, C., Starling, J. E., and Weiss, J. (2020+). VCBART: Bayesian trees for varying coefficients. Invited revision at Journal of the American Statistical Association.
[Preprint] [R package] 
Bai, R. (2020+). A unified computational and theoretical framework for highdimensional Bayesian additive models. Under review at Statistica Sinica.
[Preprint] 
Bai, R., Boland, M. R., and Chen, Y. (2020+). Fast algorithms and theory for highdimensional Bayesian varying coefficient models.
[Preprint] [R package] 
Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2020+). A robust Bayesian Copas selection model for quantifying and correcting publication bias.
[Preprint] [R package] 
Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mowery, D., and Herman, D. (2020+). A method to link neighborhoodlevel covariates to COVID19 infection patterns in Philadelphia using spatial regression. Under review at AMIA 2021 Virtual Informatics Summit.

Meeker, J. R., Canelón, S. P., Bai, R., Levine, L. D., and Boland, M. R. (2020+). Individual and neighborhoodlevel risk factors for severe maternal morbidity. Under review at Obstetrics and Gynecology.
2020

Bai, R.*, Moran, G. E.*, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2020+). Spikeandslab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association (in press).
[Paper] [Supplement] [R package] 
Bai, R., Ročková, V., and George, E. I. (2020+). Spikeandslab meets LASSO: A review of the spikeandslab LASSO. Handbook of Bayesian Variable Selection, Tadesse, M. and Vannucci, M. eds. Chapman & Hall/CRC Press (accepted pending minor revision).
[Paper] [Code]
2019

Bai, R. and Ghosh, M. (2019+). On the beta prime prior for scale parameters in highdimensional Bayesian regression models. Statistica Sinica (in press).
[Paper] [Supplement] [R package] 
Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, 53: 12101233.
[Paper] [Supplement] [R package]
2018

Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[Paper] [Supplement] [R package] 
Duerr, I., Merrill, H. R., Wang, C., Bai, R., Boyer, M. J., Dukes, M. D., and Bliznyuk, N. (2018). Forecasting urban water demand with statistical and machine learning methods using large spacetime data. Environmental Modeling and Software, 102: 2938.
[link]
Selected Works in Progress

Bai, R., Jeong, S., and Ročková, V. (2020+). Minimax rates and adaptive procedures for nonparametric regression in the overdispersed exponential family.

Bai, R.*, Balocchi, C.*, Liu, J., Canelón, S., George, E. I., Chen, Y., and Boland, M. R. (2020+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes.

^{†} Bai, R. and Qin, Q. (2020+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels.