Research Interests

  1. •  Statistics on Graphs and Networks: Inference on statistics of networks, community detection in sparse networks, semi-parametric modeling of networks.


  1. •  High-Dimensional Data Analysis: Development of methods for high-dimensional data analysis, high-dimensional time-series analysis, estimation of elliptical and shape-constrained densities in high-dimensions.


  1. •  Statistical Application in Neuroscience: Analysis of multi-electrode array data using high-dimensional time-series and simultaneous equations models and network inference methods.


  1. • Statistical Application in Epidemiology: Analysis of long-range dispersal in plant epidemiology, analysis of COVID-19 disease related data.


  1. •  Unsupervised learning, Clustering, and Manifold Learning: Developing a theoretical framework for clustering using using level set, clustering on high-dimensional data, unified framework for clustering algorithm.


  1. • Multiple Hypothesis Testing: Control of FWER and FDR under correlated hypothesis.


  1. • Semiparametric and Nonparametric Statistical Techniques and Time-series Analysis..


Grant support


  1. 1.  USDA NIFA grant: US-UK Collab: Long Distance Dispersal and Disease Spread Under Increased Ecological Complexity, Total Amount: $3,000,000, Time: 2022-2026, (PI: Chris Mundt, OSU).

  2. 2.  USDA SCRI grant: Enhancing Supply Chain Sustainability and Global Competitiveness for Pacific Northwest Hops, Total Amount: $4,853,908, Time: 2021-2025, (PI: Doug Walsh, WSU).

  3. 3.  NSF: CC* Team: Oregon Big Data Research and Education Team, Total Amount: $1,400,000, Time: 2020-2023, (PI: Brett Tyler, OSU).

  4. 4.  Workshop in Banff International Research Station on "New Directions in Statistical Inference on Networks and Graphs", Time: Sep 19-24, 2021, (PI: Sharmodeep Bhattacharyya, OSU and co-PI: Elizaveta Levina, UMich, co-PI: Carey Priebe, JHU, co-PI: Tianxi Li, UVirginia).

  5. 5.  DARPA grant: Predictive and Interpretive Analysis of Neural Time-Series Data, Amount: $50,000, Time: 2017-2018, (PI: Kenneth Kosik, UCSB and co-PI: Kristofer Bouchard, LBNL).

  6. 6.  Oregon BEST grant: Integrated Decision Support for Irrigation Management, Amount: $20,000, Time: 2016-2018, (PI: Clinton Shock, OSU).

  7. 7.  USDA NIFA grant: A production system for high value crops at risk from downy mildew: Integrating detection, breeding, extension, and education, Amount: $150,000, Time: 2016-2019, (PI: Mary Hausbeck, MSU).

  8. 8.  UC Davis grant: Network Characteristics and Modeling of Powdery Mildew Spread: Foundations for Area-Wide IPM, Amount: $20,000, Time: 2016-2017, (PI: David Gent, USDA and OSU).


Papers


Methodological


  1. 1.  Hwang, N., Xu, J., Chatterjee, S., and Bhattacharyya, S., 2023. On the estimation of the number of communities for sparse networks. Journal of the American Statistical Association, just-accepted (2023): pp. 1-22.

  2. 2.  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.

  3. 3.  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).

  4. 4.  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).

  5. 5.  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.

  6. 6.  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). Springer, Cham.

  7. 7.  Bhattacharyya, S. and Bickel, P.J., 2016. Spectral clustering and block models: A review and a new algorithm. Statistical Analysis for High-Dimensional Data (pp. 67-90). Springer, Cham.

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


      9. Applied


      1. 1.  Hwang, N., Chatterjee, S., Di, Y. and Bhattacharyya, S., 2023. Detection of Temporal Shifts in Semantics Using Local Graph Clustering. Machine Learning and Knowledge Extraction, 5(1), pp.128-143.

      2. 2.  Senn, S., Bhattacharyya, S., Presley, G., Taylor, A.E., Stanis, R., Pangell, K., Melendez, D. and Ford, J., 2023. The Community Structure of eDNA in the Los Angeles River Reveals an Altered Nitrogen Cycle at Impervious Sites. Diversity, 15(7), p.823.

      3. 3.  Joshi, M., Di, Y., Bhattacharyya, S., and Chatterjee, S., 2022. Changes over Time in Association Patterns between Estimated COVID-19 Case Fatality Rates and Demographic, Socioeconomic and Health Factors in the US States of Florida and New York. COVID, 2(10), pp.1417-1434.

      4. 4.  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 health, 22(1), p.747.

      5. 5.  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.

      6. 6.  Senn, S., Bhattacharyya, S., Presley, G., Taylor, A.E., Nash, B., Enke, R.A., Barnard-Kubow, K.B., Ford, J., Jasinski, B. and Badalova, Y., 2022. The functional biogeography of eDNA metacommunities in the post-fire landscape of the Angeles national forest. Microorganisms, 10(6), p.1218.

      7. 7.  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.


          8. Preprints and Arxiv


          1. 1. Sachdeva, P., Bak, J.H., Livezey, J., Kirst, C., Frank, L., Bhattacharyya, S., and Bouchard, K.E. 2023. Resolving Non-identifiability Mitigates Biases in Models of Neural Tuning and Functional Coupling. (bioRxiv, pp.2023-07). .

          2. 2. Ruiz, T.D., Bhattacharyya, S., and Emerson, S.C., 2023. Sparse estimation of parameter support sets for generalized vector autoregressions by resampling and model aggregation. ArXiv:2307.09684.

          3. 3. Chatterjee, S., Chatterjee, S., Mukherjee, S.S., Nath, A. and Bhattacharyya, S. 2022. Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis. ArXiv:2208.01365.

          4. 4. 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:2009.02112. .

          5. 5. Bhattacharyya, S. and Chatterjee, S., 2020. General Community Detection with Optimal Recovery Conditions for Multi-relational Sparse Networks with Dependent Layers. ArXiv:2004.03480.

          6. 6. 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:1908.11464..

          7. 7. Bhattacharyya, S., and Chatterjee, S., 2018. Spectral clustering for multiple sparse networks: I. ArXiv:1805.10594. .

          8. 8. Bhattacharyya, S. and Bickel, P.J., 2014. Community detection in networks using graph distance. Arxiv: 1401.3915. .

          9. 9. Subsampling bootstrap of count features of stochastic networks, 2012. Arxiv: 1312.2645 (With Prof. Peter Bickel) (under revision)

          10. 10.  Bhattacharyya, S. and Bickel, P.J., 2014. Adaptive estimation in elliptical distributions with extensions to high dimensions. preprint

          11. 11. Bhattacharyya, S. and Bickel, P.J., 2011. Non-parametric modeling of stochastic networks slides

          12. 12.  Bhattacharyya, S. and Bickel, P.J., 2011. A naive approach to finding number of clusters in partitioning clustering., 2011 (With Prof. Peter Bickel). preprint


          Conference Papers


          Refereed


          1. 1.  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.

          2. 2.  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.

          3. 3.  Bouchard, K., Sachdeva, P., Bhattacharya, S., Balasubramanian, M. and Ubaru, S., 2019. Union of Intersections (UoI) for interpretable data driven discovery and prediction in neuroscience. Cosyne Abstracts 2019, Lisbon, PT.

          4. 4.  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.

          5. 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. Advances in Neural Information Processing Systems (pp. 1078-1086).


          Non-Refereed


          1. 1.  Niyaghi, F., Bhattacharyya, S., and Emerson, S., 2017. Variable Selection Using Intersection and Average of Random Forests. JSM 2017 Proceedings.

          2. 1.  Bhattacharyya, S., Richards, J.W., Rice, J., Starr, D.L., Butler, N.R. and Bloom, J.S., 2012. Identification of outliers through clustering and semi-supervised learning for all sky surveys. Statistical Challenges in Modern Astronomy V (pp. 483-485). Springer, New York, NY. link


          PHD Thesis


          A Study of High-dimensional Clustering and Statistical Inference of Networks. PhD Thesis. University of California, Berkeley. link


          Applied Projects


          1. 1. Analysis of time-course CAGE data in FANTOM consortium, (With Taly Arbel and Ben Brown, LBNL).

          2. 2.  Analysis of Neural Data of Speech Articulation, (With Kristofer Bouchard, LBNL).


          Working Papers


          1. 1.  FWER control for correlated hypothesis, (With Prof. Yoav Benjamini).

          2. 2.  Theoretical Analysis of Clustering: Attempting to give a theoretical foundation to clustering by linking metric and density-based methods and thus developing better clustering algorithms for both low and high-dimensional datasets. (With Prof. Peter Bickel)

          3. 3.  Outlier Detection in Light Curve Data using Clustering, (With Dr. Joseph Richards and Prof. John Rice).