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My research interests lie in several fields of statistics including statistical inference of networks, high-dimensional statistical inference, clustering, semiparametric inference, and hypothesis testing. I am also interested in the application of statistical methods in neuroscience, genomics, and astronomy.
Before joining Oregon State University in Spring 2015, I was a post-doctoral research scholar in the Department of Statistics, University of California, Berkeley. I am also involved in projects with the Life Sciences Division, Lawrence Berkeley National Laboratory. I completed my Ph.D. in Statistics from the University of California, Berkeley in 2013 under the supervision of Prof. Peter J. Bickel. Before that, I completed B.Stat and M.Stat from Indian Statistical Institute, Kolkata.
1. Hwang, N., Xu, J., Chatterjee, S., Bhattacharyya, S., 2021. The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data. Sankhya A (2021): 1-38. (Invited paper in Sankhya Series A for Special Issue on Networks).
2. Li, T., Lei, L., Bhattacharyya, S., Van den Berge, K., Sarkar, P., Bickel, P. J., and Levina, E., 2020. Hierarchical community detection by recursive bi-partitioning. Journal of the American Statistical Association. 2020 Oct 8:1-39.
3. Bhattacharyya, S., and Chatterjee, S., 2020. Consistent Recovery of Communities from Sparse Multi-relational Networks: A Scalable Algorithm with Optimal Recovery Conditions. Complex Networks XI (pp. 92-103) 2020. Springer, Cham.
4. Ruiz, T., Balasubhramanian, M., Bouchard, K. and Bhattacharyya, S., 2020. Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models. Proceedings of 2nd Learning For Dynamics & Control (L4DC) Conference.
5. Bouchard, K., Bujan, A., Roosta-Khorasani, F., Ubaru, S., Prabhat, M., Snijders, A., Mao, J.H., Chang, E., Mahoney, M.W. and Bhattacharya, S., 2017. Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. In Advances in Neural Information Processing Systems (pp. 1078-1086).
6. Bhattacharyya, S., and Bickel, P.J., 2015. Subsampling bootstrap of count features of networks. Annals of Statistics, 43(6), pp.2384-2411.
7. Bhattacharyya, S., and Bickel, P.J., 2016. Spectral clustering and block models: A review and a new algorithm. In Statistical Analysis for High-Dimensional Data (pp. 67-90). Springer, Cham.
1. Balasubramanian, M., Ruiz, T., Cook, B., Bhattacharyya, S., Shrivastava, A., and Bouchard, K., 2020. Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. Proceedings of 34th IEEE International Parallel and Distributed Processing Symposium.
2. Gent, D. H., Bhattacharyya, S., and Ruiz, T., 2019. Prediction of Spread and Regional Development of Hop Powdery Mildew: A Network Analysis. Phytopathology, 109(8), 1392-1403.
3. Sachdeva, P. S., Bhattacharyya, S., and Bouchard, K. E., 2019. Sparse, Predictive, and Interpretable Functional Connectomics with UoI Lasso. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1965-1968). IEEE.
4. Bhattacharyya, Sharmodeep, et al. "Identification of Outliers Through Clustering and Semi-supervised Learning for All Sky Surveys." Statistical Challenges in Modern Astronomy V. Springer New York, 2012. 483-485.
1. Hwang N., Chatterjee, S., Di, Y., and Bhattacharyya, S., 2020. Estimating the treatment effect of the juvenile stay-at-home order on SARS-CoV-2 infection spread in Saline County, Arkansas. arXiv preprint arXiv:2009.08691. 2020 Sep 18.
2. Bhattacharyya S., Chatterjee S., Mukherjee S.S., 2020. Consistent detection and optimal localization of all detectable change points in piecewise stationary arbitrarily sparse network-sequences. arXiv preprint arXiv:2009.02112. 2020 Sep 4.
3. Bhattacharyya, S., and Chatterjee, S., 2020. General Community Detection with Optimal Recovery Conditions for Multi-relational Sparse Networks with Dependent Layers. arXiv preprint arXiv:2004.03480.
4. Ruiz, T., Balasubramanian, M., Bouchard, K. E., and Bhattacharyya, S., 2019. Sparse, Low-bias, and Scalable Estimation of High Dimensional Vector Autoregressive Models via Union of Intersections. arXiv preprint arXiv:1908.11464.
5. Balasubramanian, M., Ruiz, T., Cook, B., Bhattacharyya, S., Shrivastava, A., and Bouchard, K., 2018. Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale. arXiv preprint arXiv:1808.06992.
6. Bhattacharyya, S., and Chatterjee, S., 2018. Spectral clustering for multiple sparse networks: I. arXiv preprint arXiv:1805.10594.
7. Bhattacharyya, Sharmodeep, and Peter J. Bickel. "Spectral Clustering and Block Models: A Review And A New Algorithm." arXiv preprint arXiv:1508.01819 (2015).
8. Bhattacharyya, Sharmodeep, and Peter J. Bickel. "Community detection in networks using graph distance." arXiv preprint arXiv:1401.3915 (2014).
9. Bhattacharyya, Sharmodeep, and Peter J. Bickel. "Adaptive Estimation in Elliptical Distributions with Extensions to High Dimensions." (2013)
10. Bhattacharyya, Sharmodeep, and Peter J. Bickel. "Detecting Number of Clusters by Testing Block Diagonal Behavior of Similarity Matrix." (2011)
Statistical Methods II (ST 552)
Methods of Data Analysis II (ST 412/512)
Applied Multivariate Statistics (ST 557)
Theory of Statistics II (ST 562)
Modern Statistical Methods for Large and Complex Data (ST 538)
American Statistical Association
Indian International Statistical Association