Sudipto Banerjee

Sudipto Banerjee
Born	(1972-10-23) October 23, 1972
Nationality	USA
Fields	Statistics
Institutions	University of California, Los Angeles, University of Minnesota, Twin Cities
Alma mater	Presidency College, Kolkata, India; Indian Statistical Institute, Kolkata; University of Connecticut, Storrs
Thesis	Multivariate Spatial Modelling in a Bayesian Setting
Doctoral advisor	Alan E. Gelfand
Known for	Bayesian hierarchical modeling, Gaussian process, spatial data analysis, Wombling
Notable awards	Mortimer Spiegelman Award

Sudipto Banerjee (born October 23, 1972) is an Indian-American statistician best known for his work on Bayesian hierarchical modeling and inference for spatial data analysis. He is currently Professor and Chair of the Department of Biostatistics in the School of Public Health at the University of California, Los Angeles (UCLA).

Early Life and Education

Banerjee was born in Kolkata, India in October 1972, the son of Sunit and Shyamali Banerjee. His father was a civil engineer and his mother hailed from a well-known family, known for their ancestral home Bakulia House in Kidderpore, Kolkata, who were former owners of Inchek Tyres (now Tyre Corporation of India, Ltd) and active in the Kolkata real estate business. Banerjee spent a part of his childhood with his parents in Puerto Ordaz, Venezuela, where his father was employed by SIDOR. The family returned to Kolkata in 1979 and Banerjee attended Don Bosco School, Park Circus in Kolkata, graduating from high school in 1991. He attended Presidency College, Kolkata, obtaining a B.S. (Honours) in 1994, and the Indian Statistical Institute, graduating with an M.STAT in 1996. Subsequently, he moved to the United States and obtained an MS and PhD in Statistics from the University of Connecticut in 2000,^[1] where he was introduced to Bayesian statistics and hierarchical modeling by Alan Enoch Gelfand^[1] who had been instrumental in the development of the Gibbs sampler and Markov chain Monte Carlo algorithms in Bayesian statistics.

Career

Banerjee joined the University of Minnesota, Twin Cities in 2000 as an Assistant Professor of Biostatistics and was associated with the School of Public Health for 14 years. There he worked on a number of problems and wrote numerous articles on spatial statistics, developing theory and methods related to Bayesian modeling and inference for geographic data with wide-ranging applications in public and environmental health sciences, ecology, forestry, real estate economics and agronomy. In 2014, Banerjee joined the Department of Biostatistics in the School of Public Health at UCLA as Professor and Chair of Biostatistics.^[2]

Banerjee has made a number of fundamental contributions in spatial statistics, developing theory and methods for carrying out principled Bayesian inference for rates of change on spatial processes, an area often referred to as Wombling. He is also widely credited with the development and application of Bayesian hierarchical models to geo-referenced survival analysis or spatial survival analysis and for multiple disease mapping in spatial epidemiology. Banerjee is also widely known for his work on developing Bayesian models and inference for large to massive geo-spatial data, especially the Gaussian Predictive Process, the Nearest-Neighbor Gaussian Process (NNGP) and their variants that offer computational scalability to highly complex and computationally expensive Gaussian processes.

Banerjee, in collaboration with statisticians Bradley P. Carlin and Alan E. Gelfand, has authored an influential textbook Hierarchical Modeling and Analysis for Spatial Data, Second Edition^[3] that offered a comprehensive treatment of Bayesian inference for spatial data. In addition, he has authored another textbook, Linear Algebra and Matrix Analysis for Statistics,^[4] with Anindya Roy.

Awards and honors

Banerjee has received many honors, including the Abdel El-Shaarawi Award from The International Environmetric Society (TIES), the Mortimer Spiegelman Award from the American Public Health Association, elected membership of the International Statistical Institute, elected fellowships in the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA), and a Distinguished Achievement Medal from the ASA's Section on Statistics and the Environment.^[2]

Selected Works

Banerjee, Sudipto; Carlin, Bradley P.; Gelfand, Alan E. (2014), Hierarchical Modeling and Analysis for Spatial Data, Second Edition, Monographs on Statistics and Applied Probability (2nd ed.), Chapman and Hall/CRC, ISBN 9781439819173
Banerjee, Sudipto; Roy, Anindya (2014), Linear Algebra and Matrix Analysis for Statistics, Texts in Statistical Science (1st ed.), Chapman and Hall/CRC, ISBN 978-1420095388
Banerjee, S. and Gelfand, A.E. (2006) Bayesian Wombling: Curvilinear Gradient Assessment Under Spatial Process Models. Journal of the American Statistical Association, 101(476), 1487–1501. doi:10.1198/016214506000000041
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.doi:10.1111/j.1467-9868.2007.00612.x
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. doi:10.1111/j.1467-9868.2008.00663.x
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, doi:10.1080/01621459.2015.1044091

References

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