Contact Info

Primary Title: 
Associate Professor
Email Contact: 
Voice Contact: 
(541) 737-1670
Weniger Hall, Room 271


I am an Associate Professor in the Department of StatisticsOregon State University

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 StatisticsUniversity 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.


Research Interests

  • Statistics on graphs and networks.
  • Statistical application in Neuroscience.
  • Statistical Genetics: Formation and analysis of biological networks.
  • High-dimensional data analysis and method development.
  • Structured density estimation and Anomaly detection.
  • Unsupervised learning especially clustering and manifold learning.
  • Application of Statistics in Astronomy and Astrophysics.
  • Multiple hypothesis testing.
  • Semiparametric and nonparametric statistical techniques and Time-series analysis.




1. Kumar, A., Bhattacharyya, S. and Bouchard, K., 2022. Numerical characterization of support recovery in sparse regression with correlated design. Communications in Statistics-Simulation and Computation, pp.1-15.

2. Ojwang, A.M.E., Ruiz, T.D., Bhattacharyya, S., Chatterjee,S., Ojiambo, P., Gent, D., 2021. General Framework for Spatio-temporal Modeling of Epidemics with Multiple Epicenters: Application to an Aerially Dispersed Plant Pathogen. Frontiers in Applied Mathematics and Statistics (Editor’s Choice Best Paper Award, 2021)

3. 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).

4. 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.

5. 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.

5. 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.

6. 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).

7. Bhattacharyya, S., and Bickel, P.J., 2015. Subsampling bootstrap of count features of networks. Annals of Statistics, 43(6), pp.2384-2411.

8. 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. Madlock-Brown, C., Wilkens, K., Weiskopf, N., Cesare, N., Bhattacharyya, S., Riches, N.O., Espinoza, J., Dorr, D., Goetz, K., Phuong, J. and Sule, A., 2022. Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses. BMC public health22(1), pp.1-13.

2. Hwang, N., Chatterjee, S., Di, Y., and Bhattacharyya, S., 2022. Observational study of the effect of the juvenile stay-at-home order on SARS-CoV-2 infection spread in Saline County, Arkansas. Statistics and Public Policy.

3. 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.

4. 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.

5. 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.

6. 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.


Methodological (Preprint)

1. Hwang, N., Xu, J., Chatterjee, S. and Bhattacharyya, S., 2020. Estimation of Number of Communities in Assortative Sparse Networks.

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)


Professional Societies

American Statistical Association
Indian International Statistical Association



PhD in Statistics, University of California at Berkeley, 2013.
M.Stat, Indian Statistical Institute, Kolkata, 2008.
B.Stat, Indian Statistical Institute, Kolkata, 2006.