![](/wp-content/uploads/AMBA-logo-Acc-black.png"){kind=logo}
| Name | Sayantan Banerjee |
| Qualification | M.Stat. (Indian Statistical InstituteKolkata), Ph.D. (North Carolina State University) |
| Contact No. | 0731-2439568 |
| sayantanb@iimidr.ac.in | |
| Curriculum Vitae | Download |
- Brief Profile
- Research Publications
Dr. Sayantan Banerjee is working as an Associate Professor in the area of Operations Management & Quantitative Techniques at IIM, Indore. He is also the current Research & Publications Chair at the Institute.
He holds a Ph.D. in Statistics from North Carolina State University, Raleigh, USA. He specializes in Bayesian Statistics, and his Ph.D. thesis focused on Bayesian inference for high-dimensional models, dealing with both theoretical properties and computational issues for high-dimensional statistical models, with a special focus on high-dimensional graphical models. His Ph.D. 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 IIM Indore, 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 the Indian Statistical Institute, Kolkata and a B.Sc. in Statistics (Hons.) from St. Xavier’s College, Kolkata under the University of Calcutta.
Dr. Banerjee was supported by the INSPIRE Faculty Fellowship Award presented by DST, Govt of India, from 2016 to 2021 and he also held the sixth IIM Indore Young Faculty Research Chair position from 2019 to 2021. Apart from the award for best Ph.D. dissertation, he has won several research and travel awards including highly competitive awards like the Best Student Paper Award in Bayesian Statistical Science, presented by the American Statistical Association, and travel awards from the National Science Foundation (NSF).
His research interests include Bayesian inference, Graphical models, High-dimensional models, Multivariate analysis, Bayesian Asymptotics, Bayesian methods in Biostatistics, and Bayesian Machine Learning.
His teaching interests include Statistical methods, Statistical Inference, Bayesian Inference, Computational Statistics, and Machine Learning methods.
Personal website: https://sites.google.com/site/sayantaniimi/
Published/Accepted
- Curtis, S. M., Banerjee, S. and Ghosal, S. (2014). Fast Bayesian Model Assessment for Non-parametric Additive Regression. Computational Statistics and Data Analysis, Vol. 71, pp. 347 -- 358. {link}
- 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, pp. 2111 -- 2137. {link} Winner of the SBSS Student Paper Award, presented by ISBA, Joint Statistical Meetings 2013.
- Banerjee, S. and Ghosal, S. (2014). Bayesian variable selection in generalized additive partial linear models. Stat, Vol. 3, Issue 1, pp. 363 -- 378. {link}
- Banerjee, S. and Ghosal, S. (2015). Bayesian structure learning in graphical models. Journal of Multivariate Analysis, Vol. 136, pp. 147 -- 162. {link}
- Saha, A., Banerjee, S., Kurtek, S., Narang, S., Lee, J., Rao, G., Martinez, J., Bharath, K., Rao, A.U.K., Baladandayuthapani, V. (2016). DEMARCATE: Density-based Magnetic Resonance Image Clustering for assessing Tumor Heterogeneity in Cancer. NeuroImage:Clinical, Vol. 12, pp. 132 -- 143. {link}
- Banerjee, S. (2017). Posterior Convergence Rates for high-dimensional precision matrix estimation using G-Wishart priors. Stat, Vol. 6, Issue 1, pp. 207 --217. {link}
- Banerjee, S. and Ghosal, S. (2017). Discussion of "Sparse graphs using exchangeable random measures" by Francois Caron and Emily Fox. Journal of the Royal Statistical Society Series B, Vol. 79, No. 5, pp. 1343.
- Ha, M. J., Banerjee, S., Akbani, R., Liang, H., Mills, G.B., Do, K. A., and Baladandayuthapani, V. (2018). Personalized Integrated Network Modeling of The Cancer Proteome Atlas. Scientific Reports, Vol. 8(1), 14924.
- Banerjee, S., Akbani, R. and Baladandayuthapani, V. (2019). Spectral Clustering via Sparse Graph Structure Learning with application to Proteomic Signaling Networks in Cancer. Computational Statistics and Data Analysis: Special Issue on Biostatistics, Vol. 132, pp. 46 -- 69. {link}
- Banerjee, S. and Guhathakurta, K. (2020), Change-point analysis in Financial Networks. Stat, Vol. 9(1), e269.
- Banerjee, S. and Shen, W. (2022). Graph signal de-noising using t-shrinkage priors. Journal of Statistical Planning and Inference, Vol. 219, pp. 279 -- 305.
- Bhattacharyya, R., Banerjee, S., Mohammed, S., and Baladandayuthapani, V. (2022). Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India. Journal of the Indian Statistical Association: Special Issue on Spatio-temporal modeling, to appear.
- Banerjee, S. (2022). Horseshoe shrinkage methods for Bayesian fusion estimation. Computational Statistics and Data Analysis, Vol. 174, pp. 107450.
Book Chapters
- 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, pp. 175 - 192. {link}
- Banerjee, S., Castillo, I. and Ghosal, S. (2021). Bayesian Inference in High-dimensional models. Springer Volume on Data Science in collaboration with International Indian Statistical Association. (Accepted, to be published soon).
Under Revision
- Kheera, S., Banerjee, S., Datta, J., and Bhadra, A. (2021+). Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual. Winner, International Biometric Society Eastern North American Region's (ENAR) Distinguished Student Paper Awards, ENAR 2022.
- Bhattacharya, R., Burman, A., Singh, K., Banerjee, S., Maity, S., Auddy, A., Raut, S.K., Lahoti, S., Panda, R.M., and Baladandayuthapani, V. (2021+). Role of Multi-resolution Vulnerability Indices in Covid-19 spread: A Case Study in India.
- Burman, A. and Banerjee, S. (2021+). High-dimensional Portfolio Optimization using Joint Shrinkage.
- Banerjee, S. and Singh, G. (2021+). Impact of Covid-19 on the pharmaceutical industry in India. Book chapter.
- Jiang, X., Livas, S.M., Yin, F., Banerjee, S., Butts, C.T., and Shen, W. (2022). Structure recovery and trend estimation for dynamic network analysis.
- Bhadra, A., Kheera, S., Datta, J., and Banerjee, S. (2022). Graphical Evidence.