Research Topics

Methodology Development

Scalable Spatial/Spatiotemporal Data Modeling

• Zhang, L., Tang, W. & Banerjee, S. (2025+) Bayesian Geostatistics Using Predictive Stacking. Journal of the American Statistical Association (Theory and Methods) (In Press) doi:10.1080/01621459.2025.2566449 [link] [arXiv preprint]
• [preprint] Zhang L. ProjMC²: Scalable and Stable Posterior Inference for Bayesian Spatial Factor Models with Application to Spatial Transcriptomics. [arXiv preprint], 2025
• 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 135, 104224 doi:10.1016/j.jag.2024.104224 [link]
• [preprint] Pan, S., Zhang, L., Bradley, J., Banerjee, S. Bayesian Inference for Spatial-temporal Non-Gaussian Data Using Predictive Stacking, [arXiv preprint], 2024
• Zhang, L.*, Tang, W.* & Banerjee, S. (2023) Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods. Statistica Sinica doi:10.5705/ss.202021.0428 [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 83(5), 1044–1070 doi:10.1111/rssb.12472 [link]
• Zhang, L. & Banerjee, S. (2021). Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data. Biometrics 78(2), 560-573 doi:10.1111/biom.13452 [link]
• Zhang, L., Banerjee, S. & Finley, A. O. (2021). High‐dimensional multivariate geostatistics: A Bayesian matrix‐normal approach. Environmetrics 32(4), e2675 doi:10.1002/env.2675 [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 12(3), 197-209 doi:10.1002/sam.11413 [link]

Bayesian Methodology

• Magnusson, M., Torgander, J., Bürkner, P., Zhang, L., Carpenter, B., Vehtari, A. (2025) posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (oral presentation (Top 2%)). PMLR: 258:1198-1206 [link]
• Zhang, L., Carpenter, B., Gelman, A. & Vehtari, A. (2022) Pathfinder: Parallel quasi-Newton variational inference Journal of Machine Learning Research 23(306), 1-49 [link]
• Zhang, L. (2022) Applications of Conjugate Gradient in Bayesian computation. Wiley StatsRef-Statistics Reference Online 1–7 doi:10.1002/9781118445112.stat08411 [link]

Others

• Sparkes, S.†, Garcia, E. & Zhang, L. (2024). The functional average treatment effect. Journal of Causal Inference 12(1) doi:10.1515/jci-2023-0076 [link]
• [preprint] Sparkes, S.† & Zhang, L. Properties and Deviations of Random Sums of Densely Dependent Random Variables, [arXiv preprint], 2023

Applications

Environmental Health

• 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]
• 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 35(3), 425–436 doi:10.1038/s41370-024-00742-2 [link]
• [preprint] Lu, Y., Zhang, X., Neyestani, S., Li, X., Jing, L., Zhang, L., Habre, R., Zhang, J. Assessing Indoor Versus Outdoor PM2.5 Concentrations During the 2025 Los Angeles Fires Using the PurpleAir Sensor Network. [earth arXiv], 2025
• 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 17(3), e1008837 doi:10.1371/journal.pcbi.1008837 [link]
• 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 33, 100418 doi:10.1016/j.epidem.2020.100418 [link]

Remote Sensing

• Liu, S.†, Zhang L., Wang, S. A novel and practical approach to generate all-weather 30-meter land surface temperature data. IGARSS 2025 (3rd place out of 340+ submissions in the student paper competition)
• Liu, S.†, Zhang L., Wang, S. End-to-End Reconstruction of High-Resolution Temperature Data Using Physics-Guided Deep Learning. ICML workshop 2025 [link]
• [preprint] Liu, S.†, Wang, S., Zhang L. Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction. [arXiv preprint], 2025
• [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], 2025
• Liu, S.† & Zhang, L. (2024) Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction. IEEE Geoscience and Remote Sensing Letters 21, 1–5 doi:10.1109/lgrs.2024.3398689 [link]

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

Software & Notes

Software Packages

• JALAJni - A JAVA package providing a java interface for lapack and blas library
• JAMAJniLite - 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

Notes and Case Studies

• 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)

Publications and Preprints by Authorship

First or Joint First Author

• Zhang, L., Tang, W. & Banerjee, S. (2025+) Bayesian Geostatistics Using Predictive Stacking. Journal of the American Statistical Association (Theory and Methods) (In Press) doi:10.1080/01621459.2025.2566449 [link] [arXiv preprint]
• [preprint] Zhang L. ProjMC²: Scalable and Stable Posterior Inference for Bayesian Spatial Factor Models with Application to Spatial Transcriptomics. [arXiv preprint], 2025
• 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 135, 104224 doi:10.1016/j.jag.2024.104224 [link]
• Zhang, L.*, Tang, W.* & Banerjee, S. (2023) Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods. Statistica Sinica doi:10.5705/ss.202021.0428 [link]
• Zhang, L., Carpenter, B., Gelman, A. & Vehtari, A. (2022) Pathfinder: Parallel quasi-Newton variational inference Journal of Machine Learning Research 23(306), 1-49 [link]
• Zhang, L. (2022) Applications of Conjugate Gradient in Bayesian computation. Wiley StatsRef-Statistics Reference Online 1–7 doi:10.1002/9781118445112.stat08411 [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 83(5), 1044–1070 doi:10.1111/rssb.12472 [link]
• Zhang, L. & Banerjee, S. (2021). Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data. Biometrics 78(2), 560-573 doi:10.1111/biom.13452 [link]
• Zhang, L., Banerjee, S. & Finley, A. O. (2021). High‐dimensional multivariate geostatistics: A Bayesian matrix‐normal approach. Environmetrics 32(4), e2675 doi:10.1002/env.2675 [link]
• 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 33, 100418 doi:10.1016/j.epidem.2020.100418 [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 12(3), 197-209 doi:10.1002/sam.11413 [link]

Student/Mentee First Author

• Liu, S.†, Zhang L., Wang, S. A novel and practical approach to generate all-weather 30-meter land surface temperature data. IGARSS 2025 (3rd place out of 340+ submissions in the student paper competition)
• Liu, S.†, Zhang L., Wang, S. End-to-End Reconstruction of High-Resolution Temperature Data Using Physics-Guided Deep Learning. ICML workshop 2025 [link]
• [preprint] Liu, S.†, Wang, S., Zhang L. Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction. [arXiv preprint], 2025
• [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], 2025
• Sparkes, S.†, Garcia, E. & Zhang, L. (2024). The functional average treatment effect. Journal of Causal Inference 12(1) doi:10.1515/jci-2023-0076 [link]
• Liu, S.† & Zhang, L. (2024) Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction. IEEE Geoscience and Remote Sensing Letters 21, 1–5 doi:10.1109/lgrs.2024.3398689 [link]
• [preprint] Sparkes, S.† & Zhang, L. Properties and Deviations of Random Sums of Densely Dependent Random Variables, [arXiv preprint], 2023

Other Collaborations

• 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. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (oral presentation (Top 2%)). PMLR: 258:1198-1206 [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 35(3), 425–436 doi:10.1038/s41370-024-00742-2 [link]
• [preprint] Lu, Y., Zhang, X., Neyestani, S., Li, X., Jing, L., Zhang, L., Habre, R., Zhang, J. Assessing Indoor Versus Outdoor PM2.5 Concentrations During the 2025 Los Angeles Fires Using the PurpleAir Sensor Network. [earth arXiv], 2025
• [preprint] Pan, S., Zhang, L., Bradley, J., Banerjee, S. Bayesian Inference for Spatial-temporal Non-Gaussian Data Using Predictive Stacking, [arXiv preprint], 2024
• 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 17(3), e1008837 doi:10.1371/journal.pcbi.1008837 [link]

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