Current Affiliations

• Senior Associate Dean for Academic Programs UCLA Fielding School of Public Health
• Professor, UCLA Department of Biostatistics
• Professor, UCLA Department of Statistics & Data Science
• Affiliate Professor, UCLA Institute of the Environment & Sustainability.

Educational History

• B.S. (Honours) Presidency College, Calcutta, India, 1994.
• M.STAT. Indian Statistical Institute, Calcutta, India, 1996.
• Ph.D. Statistics, University of Connecticut, Storrs, Connecticut, USA, 2000.

Research Interests

• Spatial statistics and Geographic Information Systems.
• Bayesian statistics and hierarchical modeling.
• Scalable Gaussian process models for BIG DATA analysis.
• Statistical computing and related software development.

Honors and awards

• 2005, Inductee: Pi Chapter of Delta Omega National Honor Society.
• 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.

Books

Hierarchical Modeling and Analysis for Spatial Data. Second Edition

Keep up to date with the evolving landscape of space and space-time data analysis and modeling. Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. → Details

Linear Algebra and Matrix Analysis for Statistics

Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra. Beginning with the rudimentary mechanics of linear systems, the book gradually develops a formal development of Euclidean vector spaces, rank and inverse of matrices, orthogonality, projections and projectors, eigenvalues, eigenvectors, different matrix decompositions (e.g., LU, Cholesky, QR, spectral, SVD, Jordan), positive definite matrices, Kronecker and Hadamard products, and concludes with accessible treatments of some more specialized topics, such as linear iterative systems, convergence of matrices, Markov chains and Page-Rank algorithms, and more general vector spaces. → Details

Handbook of Spatial Epidemiology

Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space–time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. → Details

Reports

National Academies: Affordability of National Flood Insurance Program Premiums

Affordability of National Flood Insurance Program Premiums: Report 1 is the first part of a two-part study to provide input as FEMA prepares their draft affordability framework. This report discusses the underlying definitions and methods for an affordability framework and the affordability concept and applications. Affordability of National Flood Insurance Program Premiums gives an overview of the demand for insurance and the history of the NFIP premium setting. The report then describes alternatives for determining when the premium increases resulting from Biggert-Waters 2012 would make flood insurance unaffordable. → Details

A selection of publications over the last 5 years

Li, D., Tang, W. and Banerjee, S. (in press). Inference for Gaussian Processes with Matern covariogram on compact Riemannian manifolds. Journal of Machine Learning Research. arxiv and DOI: In process.

Alaimo Di Loro, P., Mingione, M., Lipsitt, J., Batteate, C.M., Jerrett, M.B. and Banerjee, S. (in press). Bayesian hierarchical modeling and analysis for physical activity trajectories using wearable devices data. Annals of Applied Statistics. arxiv and DOI: In process.

Halder, A., Banerjee, S. and Dey, D.K. (in press). Bayesian modeling with spatial curvature processes. Journal of the American Statistical Association. arxiv and DOI.

Gao, L., Banerjee, S. and Ritz, B. (in press). Spatial difference boundary detection for multiple outcomes using Bayesian disease mapping. Biostatistics. 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.

Finley, A.O., Datta, A., Cook, B.C., Morton, D.C. Andersen, H.E. and Banerjee, S. (2019). Efficient algorithms for Bayesian nearest-neighbor Gaussian processes. Journal of Computational and Graphical Statistics, 28, 401--414. arxiv and DOI.

Guhaniyogi, R. and Banerjee, S. (2018). Meta-Kriging: Scalable Bayesian modeling and inference for massive spatial datasets. Technometrics, 60, 430--444. DOI.