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

  • Statistical modeling and analysis of geographically referenced datasets.
  • Bayesian statistics (theory and methods) and hierarchical modelling.
  • 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).


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

Selected publications

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

Guhaniyogi, R. and Banerjee, S. (in press). Meta-Kriging: Scalable Bayesian modeling and inference for massive spatial datasets. Technometrics. DOI

Bose, M., Hodges, J.S. and Banerjee, S. (2018). Toward a diagnostic toolkit for linear models with Gaussian-process distributed random effects. Biometrics, 74, 863–873. arxiv and DOI

Banerjee, S. (2017). High-dimensional Bayesian geostatistics. Bayesian Analysis, 12, 583--614. arxiv and DOI

Datta, A., Banerjee, S., Finley, A.O., Hamm, N.A.S. and Schaap, M. (2016). Non-separable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with application to particulate matter analysis. Annals of Applied Statistics, 10, 1286--1316. arxiv and DOI

Datta, A., Banerjee, S., Finley, A.O., and Gelfand, A.E. 2016. Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111, 800--812. arxiv and DOI

Quick, H., Banerjee, S. and Carlin, B.P. (2015). Bayesian modeling and analysis for gradients in spatiotemporal processes. Biometrics, 71, 575--584. pdf (full text) and supplementary material

Monteiro, J.V., Banerjee, S. and Ramachandran, G. (2014). Bayesian modeling for physical processes in industrial hygiene using misaligned workplace data. Technometrics, 56, 238-247.

Ren, Q. and Banerjee, S. (2013). Hierarchical factor models for large spatially misaligned datasets: A low-rank predictive process approach. Biometrics, 69, 19-30.

Quick, H., Banerjee, S. and Carlin, B.P. (2013). Modeling temporal gradients in regionally aggregated California asthma hospitalization data. Annals of Applied Statistics, 7, 154-176.

Finley, A.O., Banerjee, S. and MacFarlane, D.W. (2011). A hierarchical model for predicting forest variables over large heterogeneous domains. Journal of the American Statistical Association 106, 31-48.

Banerjee, S., Finley, A.O., Waldmann, P. and Ericcson, T. (2010). Hierarchical spatial process models for multiple traits in large genetic trials. Journal of the American Statistical Association, 105, 506-521.

Zhang, Y., Hodges, J.S. and Banerjee, S. (2009). Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing. Annals of Applied Statistics 3, 1805-1830.

Finley, A.O., Banerjee, S. and McRoberts, R.E. (2009). Hierarchical spatial models for predicting tree species assemblages across large domains. Annals of Applied Statistics, 3, 1052-1079.

Banerjee, S., Gelfand, A.E., Finley, A.O. and Sang, H. (2008). Gaussian predictive process models for large spatial datasets. Journal of the Royal Statistical Society Series B, 70, 825--848.

Jin, X., Banerjee, S. and Carlin, B.P. (2007). Order-free coregionalized lattice models with application to multiple disease mapping. Journal of the Royal Statistical Society Series B, 69, 817-838.

Contact Information

Room 51-254B CHS
Department of Biostatistics
UCLA School of Public Health
Los Angeles, CA 90095-1772
Email: sudipto (at)
Phone: (310) 825-5916
Fax: (310) 267-2113

Keynote Lectures, Short Courses and Workshops

Bayesian Statistics in the BIG DATA Era, November 26--30, 2018, CIRM (Marseille Luminy, France)

High-Dimensional Bayesian Geostatistics (On Your Laptop) → Details

Doctoral Program in Statistics and Applied Probability, September 03-06, 2017, Villars-sur-Ollon, Switzerland

High-dimensional Bayesian Geostatistics → Details

CBMS Regional Conference on Spatial Statistics, August 14-18, 2017, University of California Santa Cruz (UCSC)

Bayesian modeling for spatial and spatio-temporal data → Details

Joint Statistical Meetings, July 31, 2017, Baltimore, Maryland

Bayesian modeling and inference for high-dimensional spatial-temporal data → Details

6th International Workshop on Climate Informatics, September 22-23, 2016, National Center for Atmospheric Research (NCAR), Boulder, Colorado

Bayesian modeling and inference for high-dimensional spatial-temporal data → Details

40th Annual Summer Institute of Applied Statistics, June 17-19, 2015, Brigham Young University, Provoh, Utah

Hierarchical Modeling and Analysis for Spatial Data. → Details

Workshop on Spatial Statistics, January 29-31, 2015, Texas A&M University, College Station, Texas

Bayesian Modeling and Inference for Large Geographically Referenced Data Sets → Details

The Machine Learning Summer School, January 07-16, 2015, University of Texas, Austin, Texas

Short Course on Bayesian Spatio-Temporal Modeling → Details