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Sudipto Banerjee

Professor and Chair of Biostatistics

Departments

DepartmentsType of Faculty
BiostatisticsFull Time
Expertise: 
Contact Information
Phone: 
310.825.5916
Fax: 
310.267.2113

Room 51-254B CHS
Department of Biostatistics

Areas of Interest: 

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).
Education: 
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
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 andDOI

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.