Sayantan Banerjee

Dr Sayantan Banerjee is working as an Assistant Professor in the area of Operations Management & Quantitative Techniques at IIM, Indore.

He holds a PhD in Statistics from North Carolina State University, Raleigh, USA. He specializes in Bayesian Statistics, and his PhD thesis focused on Bayesian inference for high-dimensional models, dealing with both theoretical properties and computational issues for high-dimensional statistical models, most importantly, for high-dimensional graphical models. His PhD thesis received the 2015 L.J. Savage Award (Honorable mention) for the best dissertation in Bayesian Statistical Science (Theory & Methods), presented by the International Society for Bayesian Analysis.

Prior to joining here, he was a Postdoctoral Fellow in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center in Houston, Texas, USA. He worked on high-dimensional biological datasets and Bayesian inference for graphical models.

Dr Banerjee holds an M.Stat degree from Indian Statistical Institute, Kolkata and B.Sc. in Statistics (Hons.) from St. Xavier’s College, Kolkata under University of Calcutta.

He has recently won the INSPIRE Faculty Fellowship Award presented by DST, Govt of India. Apart from the award for best PhD dissertation, he has won several research and travel awards including the highly competitive awards like the Best Student Paper Award in Bayesian Statistical Science, presented by the American Statistical Association, and the National Science Foundation (NSF) travel award for presenting in the 9th Conference on Bayesian Nonparametrics, Amsterdam, both in 2013.

His research interests include Bayesian inference, Bayesian Nonparametrics, Graphical models, High- dimensional models, and multivariate analysis.

His teaching interests include Statistical Inference, Bayesian Inference, and Statistical Methods.

Research Publications

Published

1. Curtis, S.M., Banerjee, S. and Ghosal, S. (2014) Fast Bayesian Model Assessment for Nonparametric Additive Regression, Computational Statistics and Data Analysis, Vol. 71, pages 347-358

2. Banerjee, S. and Ghosal, S. (2014) Posterior Convergence Rates for estimating large precision matrices using graphical models, Electronic Journal of Statistics, Vol 8, No. 2, pages 2111-2137

3. Banerjee, S. and Ghosal, S. (2014) Bayesian Variable Selection in generalized additive partial linear models STAT, Vol 3, No. 1, pages 363-378

4. Banerjee, S. and Ghosal, S. (2015) Bayesian Structure Learning in graphical models, Journal of Multivariate Analysis, Vol 136, pages 147-162

Book Chapters

1. Guha, S., Banerjee, S., Gu, C. and Baladandayuthapani, V. (2015) Nonparametric variable selection, clustering and prediction for large biological datasets, Nonparametric Bayesian Inference in Biostatistics, Springer, pages 175-192