Sudipto Banerjee

Sudipto Banerjee
Sudipto Banerjee 2015
Born	(1972-10-23) October 23, 1972 (age 44)
Nationality	United States
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, ASA Outstanding Statistical Application 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).

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. Banerjee attended Presidency College, Kolkata for his undergraduate studies, 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, where he was introduced to Bayesian statistics and hierarchical modeling by Alan Enoch Gelfand who had been instrumental in the development of the Gibbs sampler and Markov chain Monte Carlo algorithms in Bayesian statistics.