|Qualification||M.Stat. (Indian Statistical InstituteKolkata), Ph.D. (North Carolina State University)|
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.
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
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
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