See Google Scholar, ORCID or CV for a complete list of peer-reviewed publications.

Featured publications over the last 5 years

Coube-Sisqueille, S., Banerjee, S. and Liquet, B. (in press). Nonstationary spatial process models with spatially varying covariance kernels. Journal of Computational and Graphical Statistics. arxiv and DOI

Peruzzi, M., Banerjee, S., Dunson, D.B. and Finley, A.O. (in press). Gridding and parameter expansion for scalable latent Gaussian models of spatial multivariate data. Bayesian Analysis. arxiv and DOI

Banerjee, S., Chen, X., Frankenburg, I. and Zhou, D. (2025). Dynamic Bayesian learning for spatiotemporal mechanistic models. Journal of Machine Learning Research, 26(146), 1–43. arxiv and URL

Guhaniyogi, R., Baracaldo, L. and Banerjee, S. (2025). Bayesian data sketching for varying coefficient regression models. Journal of Machine Learning Research, 26(98), 1–29. arxiv and URL

Dey, D., Banerjee, S., Lindquist, M.A. and Datta, A. (2025). Graph-constrained analysis for multivariate functional data. Journal of Multivariate Analysis, 207, 105428. arxiv and DOI.

Li, D., Jones, A., Banerjee, S. and Engelhardt, B. (2025). Bayesian multi-group Gaussian process models for heterogeneous group-structured data. Journal of Machine Learning Research, 26(30), 1–34. arxiv and URL

Halder, A., Banerjee, S. and Dey, D.K. (2024). Bayesian modeling with spatial curvature processes. Journal of the American Statistical Association, 119, 1155–1167. arxiv and DOI.

Banerjee, S. (2024). Finite population survey sampling: An unapologetic Bayesian perspective. Sankhya A, 86, 95–124. arxiv and DOI.

Li, D., Tang, W. and Banerjee, S. (2023). Inference for Gaussian processes with Matern covariogram on compact Riemannian manifolds. Journal of Machine Learning Research, 24(101), 1–26. arxiv and URL.

Alaimo Di Loro, P., Mingione, M., Lipsitt, J., Batteate, C.M., Jerrett, M.B. and Banerjee, S. (2023). Bayesian hierarchical modeling and analysis for physical activity trajectories using wearable devices data. Annals of Applied Statistics, 17, 2865–2886. arxiv and DOI.

Gao, L., Banerjee, S. and Ritz, B. (2023). Spatial difference boundary detection for multiple outcomes using Bayesian disease mapping. Biostatistics, 24, 922–944. arxiv and DOI.

Dey, D., Datta, A. and Banerjee, S. (2022). Graphical Gaussian process models for highly multivariate spatial data. Biometrika, 109, 993–1014. arxiv and DOI.

Banerjee, S. (2022). Discussion of “Measuring housing vitality from multi-source big data and machine learning”. Journal of the American Statistical Association, 117, 1063–1065. DOI.

Peruzzi, M., Banerjee, S. and Finley, A.O. (2022). Highly scalable Bayesian geostatistical modeling via meshed Gaussian Processes on partitioned domains. Journal of the American Statistical Association, 117, 969–982. arxiv and DOI.

Zhang, L. and Banerjee, S. (2022). Spatial factor modeling: A Bayesian Matrix-Normal approach for misaligned data. Biometrics, 78, 560–573. arxiv and DOI.

Tang, W., Zhang, L. and Banerjee, S. (2021). On identifiability and consistency of the nugget in Gaussian spatial process models. Journal of the Royal Statistical Society: Series B (Methodology), 83, 1044–1070. arxiv and DOI.

Abdalla, N., Banerjee, S., Ramachandran, G. and Arnold, S. (2020). Bayesian state space modeling of physical processes in industrial hygiene. Technometrics, 62, 147–160. arxiv and DOI.