My research interests span the topics of statistical modeling and analysis for geographically referenced data, Bayesian statistics (theory and methods), statistical computing and related software development.


Tang, W.*, Zhang, L.*, & Banerjee, S. On identifiability and consistency of the nugget in Gaussian spatial process models. Journal of the Royal Statistical Society Series B, accepted arXiv preprint

Zhang, L. & Banerjee, S., (2021). Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data. Biometrics. link

Zhang, L., Banerjee, S. & Finley, A. O. (2021). High‐dimensional multivariate geostatistics: A Bayesian matrix‐normal approach. Environmetrics. link

Watson, G.L., Xiong, D., Zhang, L., Zoller, J.A., Shamshoian, J., Sundin, P., Bufford, T., Rimoin, A.W., Suchard, M.A. and Ramirez, C.M., (2021+). Pandemic velocity: forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. PLOS Computational Biology, accepted. prepint

Xiong, D.*, Zhang, L.*, Watson, G.L., Sundin, P., Bufford, T., Zoller, J.A., Shamshoian, J., Suchard, M.A. and Ramirez, C.M., (2020). Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California. Epidemics link

Zhang, L., Datta, A. & Banerjee, S. (2019). Practical Bayesian modeling and inference for massive spatial data sets on modest computing environments. Statistical Analysis and Data Mining: The ASA Data Science Journal link


Zhang, L.*, Tang, W.* & Banerjee, S. Fixed-Domain Asymptotics Under Vecchia’s Approximation of Spatial Process Likelihoods. Submitted arXiv preprint

Zhang, L., Carpenter, B., Gelman, A. & Vehtari, A. Pathfinder: Parallel quasi-Newton variational inference Submitted arXiv preprint

(* co-first author)


Stan case study of Nearest neighbor Gaussian process (NNGP) based models link

A Note on using Kullback-Leibler Divergence to compare the performance of some Nearest Neighbor Gaussian Process (NNGP) based models link (HTML)