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.
Publications
Guo, F., Chen, X., Howland, S., Niu, Z., Zhang, L., Gauderman, J., McConnell, R., Pavlovic, N., Lurmann, F., Bastain, T., Habre, R., Breton, C. & Farzan, S.. (2025) Childhood Exposure to Air Pollution, Body Mass Index Trajectories, and Insulin Resistance Among Young Adults. JAMA Network Open link
Magnusson, M., Torgander, J., Bürkner, P., Zhang, L., Carpenter, B., Vehtari, A. (2025) posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. AISTATS 2025 oral presentation (Top 2%). link
Li, S., Oliva, P., Zhang, L., Goodrich, J., McConnell, R., Conti, D., Chatzi, L. & Aung, M. (2025). Associations between per-and polyfluoroalkyl substances (PFAS) and county-level cancer incidence between 2016 and 2021 and incident cancer burden attributable to PFAS in drinking water in the United States. Journal of Exposure Science & Environmental Epidemiology link
Zhang, L., Finley, A., Nothdurft, A. & Banerjee, S. (2024) Bayesian Modeling of Incompatible Spatial Data: A Case Study Involving Post-Adrian Storm Forest Damage Assessment, International Journal of Applied Earth Observation and Geoinformation link
Sparkes, S.†, Garcia, E. & Zhang, L. (2024). The functional average treatment effect. Journal of Causal Inference link
Liu, S.† & Zhang, L. (2024) Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction. IEEE Geoscience and Remote Sensing Letters link
Zhang, L.*, Tang, W.* & Banerjee, S. (2023) Fixed-Domain Asymptotics Under Vecchia’s Approximation of Spatial Process Likelihoods. Statistica Sinica link
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
Preprints
Zhang, L., Tang, W. & Banerjee, S. Bayesian Geostatistics Using Predictive Stacking. arXiv preprint
Sparkes, S.† & Zhang, L.. Properties and Deviations of Random Sums of Densely Dependent Random Variables, arXiv preprint
Pan, S., Zhang, L., Bradley, J., Banerjee, S. Bayesian Inference for Spatial-temporal Non-Gaussian Data Using Predictive Stacking, arXiv preprint
Liu, S.†, Wang, S., Zhang L.. Daily land surface temperature reconstruction in Landsat cross-track areas using deep ensemble learning with uncertainty quantification. arXiv preprint
Liu, S.†, Wang, S., Zhang L.. Uncertainty-Aware Hourly Air Temperature Mapping at 2 km Resolution via Physics-Guided Deep Learning.
(* co-first author, † students mentored by me)
Notes
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)
Packages
JALAJni JaLAJni is a JAVA package providing a java interface for lapack and blas library
JAMAJniLite JAMAJniLite is a JAVA package providing a java interface for lapack and blas libraries and using the classes defined by JAMA Package.
phase1PRMD Implements Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints and longitudinal efficacy measure from multiple treatment cycles and allows individualized dose modification.