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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).
  • 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).
  • 2021, President-Elect of the International Society for Bayesian Analysis.
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: 

Zhang, L. and Banerjee, S. (in press). Spatial factor modeling: A Bayesian Matrix-Normal approach for misaligned data. Biometrics. arxiv and DOI

Peruzzi, M., Banerjee, S. and Finley, A.O. (in press). Highly scalable Bayesian geostatistical modeling via meshed Gaussian Processes on partitioned domains. Journal of the American Statistical Association. 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

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.