Research Topics

Methodology Development

Scalable Spatial/Spatiotemporal Data Modeling

• 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 [link]
• 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 [link]
• Zhang, L., Tang, W. & Banerjee, S. Bayesian Geostatistics Using Predictive Stacking. [arXiv preprint], 2023
• 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]
• 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]

Bayesian Methodology

• 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]
• 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]

Others

• Sparkes, S.†, Garcia, E. & Zhang, L. (2024). The functional average treatment effect. Journal of Causal Inference [link]
• 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 [link]
• 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 [preprint]
• 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]

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]
• Liu, S.†, Wang, S., Zhang L. Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction. [arXiv preprint], 2025
• 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 [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. 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 [link]
• Zhang, L.*, Tang, W.* & Banerjee, S. (2023) Fixed-Domain Asymptotics Under Vecchia's Approximation of Spatial Process Likelihoods. Statistica Sinica [link]
• Zhang, L., Tang, W. & Banerjee, S. Bayesian Geostatistics Using Predictive Stacking. [arXiv preprint], 2023
• 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]
• 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]

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]
• Liu, S.†, Wang, S., Zhang L. Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction. [arXiv preprint], 2025
• 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 [link]
• Liu, S.† & Zhang, L. (2024) Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction. IEEE Geoscience and Remote Sensing Letters [link]
• 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. 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]
• 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
• 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 [preprint]

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