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.


Zhang, L.*, Tang, W.* & Banerjee, S. (2023+) Fixed-Domain Asymptotics Under Vecchia’s Approximation of Spatial Process Likelihoods. Statistica Sinica (accepted) arXiv preprint

Zhang, L., Carpenter, B., Gelman, A. & Vehtari, A. (2022) Pathfinder: Parallel quasi-Newton variational inference Journal of Machine Learning Research link

Zhang, L. (2022) Applications of Conjugate Gradient in Bayesian computation. Wiley StatsRef-Statistics Reference Online link

Tang, W.*, Zhang, L.*, & Banerjee, S. (2021) On identifiability and consistency of the nugget in Gaussian spatial process models. Journal of the Royal Statistical Society Series B link

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. 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. Exact Bayesian Geostatistics Using Predictive Stacking. arXiv preprint

Sparkes, S.† & Zhang, L.. Properties and Deviations of Random Sums of Densely Dependent Random Variables, arXiv preprint

Liu, S.† & Zhang, L.. Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction.

Zhang, L., Finley, A., Nothdurft, A. & Banerjee, S. Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment, arXiv preprint

Sparkes, S.†, Garcia, E. & Zhang, L.. The functional average treatment effect, arXiv preprint

(* co-first author, † students mentored by me)


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)