Sudipto Banerjee

Sudipto Banerjee, Ph.D., is a Professor in the Department of Biostatistics and in the Department of Statistics & Data Science with an affiliate appointment in the UCLA Institute of the Environment and Sustainability. His research expertise includes Bayesian inference and statistical machine learning methods for complex systems involving massive datasets ("BIG DATA"); environmental processes and their impact on public health; spatial data science; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis. His theoretical and methodological contributions in statistics are available in the form of several scholarly publications in highly regarded peer-reviewed journals in statistics, biostatistics and health sciences; an authoritative textbook on spatial statistics; a textbook on linear algebra for statisticians; an edited comprehensive handbook on spatial epidemiology; and two committee reports for the National Research Council of the National Academies. He has served as Principal Investigator of over 14 major federally funded research projects from NIH and NSF primarily devoted to the advancement of statistical theories and methods for space-time processes and their substantive impact. Methods developed by Professor Banerjee are being widely employed in epidemiological and environmental health research to enhance scientific understanding of how environmental factors affect human health over space and time. Dr. Banerjee has made pioneering contributions in several important areas at the interface of spatial data science and health research including spatial survival analysis; space-time mapping and analysis of multiple diseases; statistical inference for mechanistic systems; case studies in complex scientific data analysis; space-time BIG DATA analytics; and statistical computing, data science and software development. He has played prominent roles in substantive public health projects such as “The GuLF Study” (Deepwater Horizon disaster) and is currently overseeing data analysis efforts to evaluate the health effects from the massive natural gas leak disaster in Aliso Canyon in California.

Research Expertise

• Bayesian modeling and inference in complex dependent "BIG DATA" settings.
• Environmental processes and their impact on public health.
• Spatial data science and machine learning methods.
• Spatial epidemiology.
• Stochastic process models.
• Statistical learning from physical and mechanistic systems.
• Survey sampling.
• Survival analysis.
• Statistical computing and related software development.

Honors and Awards

• 2009, Abdel El Sharaawi Young Researcher Award from The International Environmetrics Society.
• 2010, Elected member, International Statistical Institute.
• 2011, Mortimer Spiegelman Award from the Statistics Section of the American Public Health Association.
• 2012, Elected Fellow of the American Statistical Association (ASA).
• 2012, International Indian Statistical Association's Young Researcher Award.
• 2015, Presidential Invited Address, Western North American Regional (WNAR) Meeting of the International Biometric Society.
• 2015, Elected Fellow of the Institute of Mathematical Statistics (IMS).
• 2015, Distinguished Achievement Medal from ASA Section on Statistics and the Environment.
• 2017, ASA Outstanding Application Award.
• 2018, Elected Fellow of the International Society for Bayesian Analysis (ISBA).
• 2019, George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS).
• 2020, Elected Fellow of the American Association for the Advancement of Science (AAAS).
• 2022, President of the International Society for Bayesian Analysis (ISBA).
• 2024, Jerome Sacks Award for Cross-Disciplinary Research from the National Institute of Statistical Science (NISS).

Selected publications (last 5 years)

• Coube-Sisqueille, S., Banerjee, S. and Liquet, B. (in press). Nonstationary spatial process models with spatially varying covariance kernels. Journal of Computational and Graphical Statistics. arxiv and DOI
• Peruzzi, M., Banerjee, S., Dunson, D.B. and Finley, A.O. (in press). Gridding and parameter expansion for scalable latent Gaussian models of spatial multivariate data. Bayesian Analysis. arxiv and DOI
• Guhaniyogi, R., Baracaldo, L. and Banerjee, S. (2025). Bayesian data sketching for varying coefficient regression models. Journal of Machine Learning Research, 26(98), 1–29. arxiv and URL
• Dey, D., Banerjee, S., Lindquist, M.A. and Datta, A. (2025). Graph-constrained analysis for multivariate functional data. Journal of Multivariate Analysis, 207, 105428. arxiv and DOI.
• Li, D., Jones, A., Banerjee, S. and Engelhardt, B. (2025). Journal of Machine Learning Research, 26(30), 1--34. arxiv and Journal Link
• Halder, A., Banerjee, S. and Dey, D.K. (2024). Bayesian modeling with spatial curvature processes. Journal of the American Statistical Association, 119, 1155-1167. arxiv and DOI.
• Alaimo Di Loro, P., Mingione, M., Lipsitt, J., Batteate, C.M., Jerrett, M.B. and Banerjee, S. (2023). Bayesian hierarchical modeling and analysis for physical activity trajectories using wearable devices data. Annals of Applied Statistics, 17, 2865-2886. arxiv and DOI.
• Li, D., Tang, W. and Banerjee, S. (2023). Inference for Gaussian Processes with Matern covariogram on compact Riemannian manifolds. Journal of Machine Learning Research, 24(101), 1-26. arxiv and Journal Link.
• Gao, L., Banerjee, S. and Ritz, B. (2023). Spatial difference boundary detection for multiple outcomes using Bayesian disease mapping. Biostatistics, 24, 922-944. arxiv and DOI .
• Dey, D., Datta, A. and Banerjee, S. (2022). Graphical Gaussian process models for highly multivariate spatial data. Biometrika, 109, 993--1014. arxiv and DOI
• Banerjee, S. (2022). Discussion of "Measuring housing vitality from multi-source big data and machine learning”. Journal of the American Statistical Association, 117, 1063–1065. DOI .
• Peruzzi, M., Banerjee, S. and Finley, A.O. (2022). Highly scalable Bayesian geostatistical modeling via meshed Gaussian Processes on partitioned domains. Journal of the American Statistical Association, 117, 969--982. arxiv and DOI
• Zhang, L. and Banerjee, S. (2022). Spatial factor modeling: A Bayesian Matrix-Normal approach for misaligned data. Biometrics, 78, 560--573. arxiv and DOI
• Tang, W., Zhang, L. and Banerjee, S. (2021). On identifiability and consistency of the nugget in Gaussian spatial process models. Journal of the Royal Statistical Society: Series B (Methodology), 83, 1044--1070. arxiv and DOI
• Abdalla, N., Banerjee, S., Ramachandran, G. and Arnold, S. (2020). Bayesian state space modeling of physical processes in industrial hygiene. Technometrics, 62, 147--160. arxiv and DOI