* = cofirst author
† = alphabetical order
Semiparametric/Nonparametric Estimation and Variable Selection
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. (2021+). A unified computational and theoretical framework for highdimensional Bayesian additive models. Invited revision at Statistica Sinica.
[Preprint] 
Bai, R., Jeong, S., and Ročková, V. (2021+). Minimax rates and adaptive procedures for generalized nonparametric regression. In preparation.
Varying Coefficient and Time Series Models

Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2021+). VCBART: Bayesian trees for varying coefficients. Invited revision at Journal of the American Statistical Association.
[Preprint] [R package] Bai, R., Boland, M. R., and Chen, Y. (2021+). Fast algorithms and theory for highdimensional Bayesian varying coefficient models. Under review at Journal of Machine Learning Research.
[Preprint] [R package]
Bai, R. (2021+). Uncertainty quantification for Bayesian vector autoregressive models. In preparation.
HighDimensional Statistics

Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in highdimensional Bayesian regression models. Statistica Sinica, 31.
[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] 
Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, 53: 12101233.
[Paper] [Supplement] [R package] 
Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[Paper] [Supplement] [R package]
MetaAnalysis

Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2021+). A robust Bayesian Copas selection model for quantifying and correcting publication bias.
[Preprint] [R package]
Spatiotemporal Modeling

Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mowery, D., and Herman, D. (2021). A method to link neighborhoodlevel covariates to COVID19 infection patterns in Philadelphia using spatial regression. AMIA 2021 Virtual Informatics Summit (accepted).
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]
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. Invited revision at Obstetrics and Gynecology.

Bai, R.*, Balocchi, C.*, Liu, J., Canelón, S., George, E. I., Chen, Y., and Boland, M. R. (2021+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes. In preparation.
Theoretical Analysis of Algorithms

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