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Sayantan Banerjee

facility overview img
Name
Sayantan Banerjee
Qualification
M.Stat. (Indian Statistical InstituteKolkata), Ph.D. (North Carolina State University)
Contact No.
0731-2439568
Email
sayantanb@iimidr.ac.in
Curriculum Vitae
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Dr. Sayantan Banerjee is an Associate Professor in the Operations Management & Quantitative Techniques area at the Indian Institute of Management Indore (IIM Indore), and the Chairperson of the Post Graduate Programme (PGP) in Management. Prior to this, he assumed the position of Research & Publications Chair at the Institute from April 2022 to March 2024. Additionally, he had the honor of holding the position of Young Faculty Research Chair at IIM Indore from January 2019 to December 2021.

Prior to his tenure at IIM Indore, Dr. Banerjee worked as a Postdoctoral fellow in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center, Houston, Texas, working with Prof.  Veerabhadran Baladandayuthapani. Dr. Banerjee has a Ph.D. degree in Statistics from North Carolina State University, Raleigh, North Carolina (2010 – 2014).

His educational foundation comprises a Bachelor’s degree with Honors in Statistics from St. Xavier’s College, Kolkata (2005 – 2008), followed by an M.Stat. degree from the Indian Statistical Institute, Kolkata (2008 – 2010).

Dr. Banerjee’s Ph.D. thesis work is on ‘Bayesian Inference for High Dimensional Models: Convergence Properties and Computational Issues’, under the supervision of Prof. Subhashis Ghoshal. The dissertation has been awarded the Leonard J. Savage Award, Honorable Mention, for the best dissertation in Bayesian Statistics (Theory and Methods) 2014, presented by the International Society for Bayesian Analysis (ISBA).

Dr. Banerjee’s primary research interests include Bayesian Inference for high-dimensional models, Graphical models, Bayesian Asymptotics, Multivariate Analysis, Biostatistics, and Bayesian Machine Learning.

Personal website: https://sites.google.com/site/sayantaniimi/home
Google Scholar: https://scholar.google.co.in/citations?user=6CKW2A0AAAAJ&hl=en

Research interests

  • Bayesian inference
  • Graphical models
  • High-dimensional models
  • Multivariate analysis
  • Bayesian Asymptotics
  • Bayesian methods in Biostatistics
  • Bayesian Machine Learning

Research Grants

  1. SERB MATRICS Grant, awarded by the Science & Engineering Research Board (SERB), Govt. of India. Duration: Jan 2023 to Dec 2025. Annual research grant of 220,000 INR.
  2. Grants to Support Research Collaboration by Visiting Other Institutions, awarded by IIM Indore, Jan 2023. Research grant of 400,000 INR.
  3. IIM Indore Young Faculty Research Chair Award. Duration: Jan 2019 to Dec 2021. Annual research grant of 200,000 INR.
  4. DST INSPIRE Faculty Award, awarded by Department of Science and Technology, Ministry of Science and Technology, Govt. of India. Duration: April 2016 to March 2021. Annual research grant of 700,000 INR.

Publications

Published/Accepted

  1. 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]
  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, pp. 2111 — 2137. [link] Winner of the SBSS Student Paper Award, presented by ISBA, Joint Statistical Meetings 2013.
  3. Banerjee, S. and Ghosal, S. (2014). Bayesian variable selection in generalized additive partial linear models. Stat, Vol. 3, Issue 1, pp. 363 — 378. [link]
  4. Banerjee, S. and Ghosal, S. (2015). Bayesian structure learning in graphical models. Journal of Multivariate Analysis, Vol. 136, pp. 147 — 162. [link]
  5. 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]
  6. 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]
  7. 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.
  8. 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. [link]
  9. 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]
  10. Banerjee, S. and Guhathakurta, K. (2020), Change-point analysis in Financial Networks. Stat, Vol. 9(1), e269.
  11. Bhattacharyya, R., Banerjee, S., Mohammed, S., and Baladandayuthapani, V. (2021). Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India.  Journal of the Indian Statistical Association: Special Issue on Spatio-temporal modeling, Vol. 59 (2).
  12.  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.
  13. Banerjee, S. (2022). Horseshoe shrinkage methods for Bayesian fusion estimation.  Computational Statistics and Data Analysis, Vol. 174, pp. 107450.
  14. Bhattacharya, R., Burman, A., Singh, K., Banerjee, S., Maity, S., Auddy, A., Raut, S.K., Lahoti, S., Panda, R.M., and Baladandayuthapani, V. (2022). Role of Multi-resolution Vulnerability Indices in Covid-19 spread: A Case Study in India. BMJ Open, Vol. 12 (11), pp. e056292.
  15. Jiang, X., Livas, S.M., Yin, F., Banerjee, S., Butts, C.T., and Shen, W. (2023). Structure recovery and trend estimation for dynamic network analysis. Stat, Vol. 12(1), e593.
  16. Sagar, K., Banerjee, S., Datta, J., and Bhadra, A. (2024). Precision Matrix Estimation under the Horseshoe-like Prior-Penalty Dual. Electronic Journal of Statistics, Vol. 18(1), pp. 1 — 46.
    Winner (Ksheera Sagar), International Biometric Society Eastern North American Region’s (ENAR) Distinguished Student Paper Awards, ENAR 2022.
  17. Datta, J., Banerjee, S., and Dunson, D.B. (2024). Nonparametric Bayes multiresolution testing for high-dimensional rare events. Journal of Nonparametric Statistics.
  18. Sagar, K., Datta, J., Banerjee, S., and Bhadra, A. (2024). Maximum a Posteriori Estimation in Graphical Models Using Local Linear Approximation. Stat, Vol. 13(2), e682.

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, pp. 175 – 192. [link]
  2. 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

  1. Burman, A. and Banerjee, S. (2021+). High-dimensional Portfolio Optimization using Joint Shrinkage. 
  2. Banerjee, S. and Singh, G. (2021+). Impact of Covid-19 on the pharmaceutical industry in India. Book chapter. 
  3. Bhadra, A., Sagar, K., Datta, J., and Banerjee, S. (2022). Graphical Evidence.
  4. Mazumder, S., Banerjee, S., and Bhattacharya, S. (2021+). A new spatio-temporal model exploiting Hamiltonian dynamics.

In Preparation

  1. Amed, S. and Banerjee, S. (2024). PDx – Boosted Credit Scoring Model in Production for Digital Lending with MLOps.
  2. Banerjee, S. (2023+). Bayesian dynamic trend filtering methods with applications in Astronomy.
  3. Amed, S. and Banerjee, S. (2023+). Federated learning for survival analysis in cancer.
  4. Chattopadhyay, A., Deb, S., Banerjee, S. and Chatterjee, S. (2022+). Statistical modeling of Teleconnections.

Current Students/Research Associates

Ph.D. students

  1. Sultan Amed. Ph.D. Student (Executive DPM) at IIM Indore (2021 —  present).
  2.  Venkata Pradeep Tatiraju. Ph.D. student (Executive DPM) at IIM Indore (2022 — present).

Others

  1. Anik Burman. PhD student at Johns Hopkins University, Baltimore, USA.
  2. Anagh Chattopadhyay. PhD student at Johns Hopkins University, Baltimore, USA.
  3. Shourjya Bhattacharya.  MS student in Data Science and Management program, IIT Indore – IIM Indore.