** = cofirst author
† = alphabetical order
Preprints

Deshpande, S. K., Bai, R., Balocchi, C., Starling, J. E., and Weiss, J. (2021+). VCBART: Bayesian trees for varying coefficients. Under review.
[Preprint] [Software] 
Bai, R. (2021+). Spikeandslab group lasso for consistent Bayesian estimation and variable selection in nonGaussian generalized additive models. Under revision.
[Preprint] [Software] 
Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2021+). A robust Bayesian Copas selection model for quantifying and correcting publication bias. Under revision.
[Preprint] [Software] 
Bai, R., Boland, M. R., and Chen, Y. (2021+). Fast algorithms and theory for highdimensional Bayesian varying coefficient models. Under review.
[Preprint] [Software] 
Balocchi, C.**, Bai, R.**, Liu, J., Canelón, S. P., George, E. I., Chen, Y., and Boland, M. R. (2021+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes. Under review.
[Preprint] [Software] 
Meeker, J. R., Burris, H. H., Bai, R., Levine, L. D., and Boland, M. R. (2021+). Neighborhood deprivation increases the risk of postinduction cesarean delivery. Under review.
2021

Bai, R.**, Moran, G. E.**, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2021). Spikeandslab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association (in press).
[Paper] [Supplement] [Software] 
Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in highdimensional Bayesian regression models. Statistica Sinica, 31: 843865.
[Paper] [Supplement] [Software] 
Bai, R., Ročková, V., and George, E. I. (2021+). Spikeandslab meets LASSO: A review of the spikeandslab LASSO. Handbook of Bayesian Variable Selection, Tadesse, M. and Vannucci, M. eds. Chapman & Hall/CRC Press (in press).
[Paper] [Software] 
Meeker, J. R., Canelón, S. P., Bai, R., Levine, L. D., and Boland, M. R. (2021). Individual and neighborhoodlevel risk factors for severe maternal morbidity. Obstetrics & Gynecology, 137: 847854.
[Paper] 
Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mellis, I., Mowery, D. L., and Herman, D. (2021). A method to link neighborhoodlevel covariates to COVID19 infection patterns in Philadelphia using spatial regression. AMIA 2021 Virtual Informatics Summit, 2021 Mar 24.
[Paper]
2019

Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, 53: 12101233.
[Paper] [Supplement] [Software]
2018

Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[Paper] [Supplement] [Software] 
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.
[Paper]
Selected Works in Progress

Bai, R., Jeong, S., and Ročková, V. (2021+). Minimax rates and adaptive procedures for generalized nonparametric regression.

Bai, R. (2021+). Uncertainty quantification for Bayesian vector autoregressive models.

Bai, R., Liu, X., Lin, L., Chu, H., and Chen, Y. (2021+). ABSORB: A Bayesian Selection model for correcting and quantifying Outcome Reporting Bias in multivariate metaanalysis.

Bai, R.^{†} and Qin, Q.^{†} (2021+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels.