Srijan Sengupta

Yanchenko, E., & Sengupta, S. (2024, March 11). A generalized hypothesis test for community structure in networks. NETWORK SCIENCE, Vol. 3. https://doi.org/10.1017/nws.2024.1

Tabaie, A., Sengupta, S., Pruitt, Z. M., & Fong, A. (2023). A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ Health & Care Informatics, 30(1), e100731. https://doi.org/10.1136/bmjhci-2022-100731

Ganguly, I., Buhrman, G., Kline, E., Mun, S. K. K., & Sengupta, S. (2023). Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing. DIAGNOSTICS, 13(7). https://doi.org/10.3390/diagnostics13071215

Yanchenko, E., & Sengupta, S. (2023). Core-periphery structure in networks: A statistical exposition. Statistics Surveys, 17(none), 42–74. https://doi.org/10.1214/23-SS141

Ray, M., Guha, S., Dhungana, R. R., Karak, A., Choudhury, B., Ray, B., … Selker, H. P. (2023). Development and validation of a predictive model for the diagnosis of rheumatic heart disease in low-income countries based on two cross-sectional studies. INTERNATIONAL JOURNAL OF CARDIOLOGY CARDIOVASCULAR RISK AND PREVENTION, 18. https://doi.org/10.1016/j.ijcrp.2023.200195

Kline, E., & Sengupta, S. (2023). How AI can Help us Understand and Mitigate Error Propagation in Radiation Oncology. In Artificial Intelligence in Radiation Oncology (pp. 305–334). https://doi.org/10.1142/9789811263545_0014

Larsen, N., Stallrich, J., Sengupta, S., Deng, A., Kohavi, R., & Stevens, N. T. (2023, October 17). Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology. AMERICAN STATISTICIAN, Vol. 10. https://doi.org/10.1080/00031305.2023.2257237

Ali, M. S., Aneja, V., Ganguly, I., Sanyal, S., & Sengupta, S. (2023, November 8). Wildfire Pollution Emissions, Exposure, and Human Health: A Growing Air Quality Control Issue. https://doi.org/10.3390/ecas2023-15922

Guo, Z., Cho, J.-H., Chen, I.-R., Sengupta, S., Hong, M., & Mitra, T. (2022). SAFER: Social Capital-Based Friend Recommendation to Defend against Phishing Attacks. Proceedings of the International AAAI Conference on Web and Social Media, 16, 241–252. https://doi.org/10.1609/icwsm.v16i1.19288

Komolafe, T., Fong, A., & Sengupta, S. (2022). Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases. Journal of Data Science, 1–20. https://doi.org/10.6339/22-JDS1038

Boxley, C., Krevat, S., Sengupta, S., Ratwani, R., & Fong, A. (2022). Using Community Detection Techniques to Identify Themes in COVID-19–Related Patient Safety Event Reports. Journal of Patient Safety, 18(8), e1196–e1202. https://doi.org/10.1097/PTS.0000000000001051

Sengupta, S., Aneja, V. P., & Kravchenko, J. (2022). Wildfire Pollution Exposure and Human Health: A Growing Air Quality and Public Health Issue. Environmental Science Proceedings, 19(1), 59. https://doi.org/10.3390/ecas2022-12809

Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., … Sparks, R. (2021, September 9). Broader impacts of network monitoring: Its role in government, industry, technology, and beyond. QUALITY ENGINEERING, Vol. 33. https://doi.org/10.1080/08982112.2021.1974036

Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., … Sparks, R. (2021). Foundations of network monitoring: Definitions and applications. Quality Engineering, 33(4), 719–730. https://doi.org/10.1080/08982112.2021.1974033

Guo, Z., Cho, J.-H., Chen, I.-R., Sengupta, S., Hong, M., & Mitra, T. (2021). Online Social Deception and Its Countermeasures: A Survey. IEEE Access, 9, 1770–1806. https://doi.org/10.1109/ACCESS.2020.3047337

Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., … Sparks, R. (2021). Research in network monitoring: Connections with SPM and new directions. Quality Engineering, 33(4), 736–748. https://doi.org/10.1080/08982112.2021.1974035

Dasgupta, A., & Sengupta, S. (2021). Scalable Estimation of Epidemic Thresholds via Node Sampling. Sankhya A, 84(1), 321–344. https://doi.org/10.1007/s13171-021-00249-0

Pruitt, Z., Boxley, C., Krevat, S., Sengupta, S., Ratwani, R., & Fong, A. (2021). The Impact of COVID-19 on Medical Device Reporting and Investigation. Patient Safety, 9, 28–35. https://doi.org/10.33940/data/2021.9.3

Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., … Sparks, R. (2021). The interdisciplinary nature of network monitoring: Advantages and disadvantages. Quality Engineering, 33(4), 731–735. https://doi.org/10.1080/08982112.2021.1974034

Guo, Z., Cho, J.-H., Chen, R., Sengupta, S., Hong, M., & Mitra, T. (2020). Online Social Deception and Its Countermeasures: A Survey. IEEE Access.

Dasgupta, A., & Sengupta, S. (2020). Scalable estimation of epidemic thresholds via node sampling. ArXiv Preprint ArXiv:2007.14820.

Kodali, L., Sengupta, S., House, L., & Woodall, W. H. (2020). The value of summary statistics for anomaly detection in temporally evolving networks: A performance evaluation study. Applied Stochastic Models in Business and Industry, 36(6), 980–1013.

Debchoudhury, S., Sengupta, S., Earle, G., & Coley, W. (2019). A Bootstrap-Based Approach for Improving Measurements by Retarding Potential Analyzers. Journal of Geophysical Research: Space Physics, 124(6), 4569–4584.

Bhadra, S., Chakraborty, K., Sengupta, S., & Lahiri, S. (2019). A Bootstrap-based Inference Framework for Testing Similarity of Paired Networks. ArXiv Preprint ArXiv:1911.06869.

Debchoudhury, S. (2019). Parameter estimation from retarding potential analyzers in the presence of realistic noise. Virginia Tech.

Komolafe, T., Quevedo, A. V., Sengupta, S., & Woodall, W. H. (2019). Statistical evaluation of spectral methods for anomaly detection in static networks. Network Science, 7(3), 319–352.

Leitch, J., Alexander, K. A., & Sengupta, S. (2019). Toward epidemic thresholds on temporal networks: a review and open questions. Applied Network Science, 4(1), 1–21. https://doi.org/10.1007/s41109-019-0230-4

Li, M. M., Sengupta, S., & Hanigan, M. D. (2019). Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen. Journal of Dairy Science, 102(10), 8850–8861.

Sengupta, S., & Chen, Y. (2018). A block model for node popularity in networks with community structure. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(2), 365–386.

Sengupta, S., & Chen, Y. (2018). A block model for node popularity in networks with community structure Series B Statistical methodology.

Sengupta, S. (2018). Anomaly detection in static networks using egonets. ArXiv Preprint ArXiv:1807.08925.

Sengupta, S., & Woodall, W. H. (2018). Discussion of “Statistical methods for network surveillance.” Applied Stochastic Models in Business and Industry, 34(4), 446–448.

Performance evaluation of social network anomaly detection using a moving window--based scan method. (2018). Quality and Reliability Engineering International, 34(8), 1699–1716.

Zhao, M. J., Driscoll, A. R., Sengupta, S., Fricker, R. D., Jr, Spitzner, D. J., & Woodall, W. H. (2018). Performance evaluation of social network anomaly detection using a moving window-based scan method. Quality and Reliability Engineering International, 34(8), 1699–1716. https://doi.org/10.1002/qre.2364

Zhao, M. J., Driscoll, A. R., Sengupta, S., Stevens, N. T., Fricker, R. D., & Woodall, W. H. (2018). The effect of temporal aggregation level in social network monitoring. PLOS ONE, 13(12), e0209075. https://doi.org/10.1371/journal.pone.0209075

The effect of temporal aggregation level in social network monitoring. (2018). Plos One, 13(12), e0209075.

Sengupta, S., Volgushev, S., & Shao, X. (2016). A subsampled double bootstrap for massive data. Journal of the American Statistical Association, 111(515), 1222–1232.

Sengupta, S. (2016). Statistical analysis of networks with community structure and bootstrap methods for big data. University of Illinois at Urbana-Champaign.

Cavaliere, G., Politis, D. N., Rahbek, A., Bertail, P., Clémençon, S., Tressou, J., & others. (2015). Recent developments in bootstrap methods for dependent data. Journal of Time Series Analysis, 36(3), 462–480.

Sengupta, S., & Chen, Y. (2015). Spectral clustering in heterogeneous networks. Statistica Sinica, 1081–1106.

Sengupta, S., Shao, X., & Wang, Y. (2015). The dependent random weighting. Journal of Time Series Analysis, 36(3), 315–326.

Sengupta, S. (2010). Modeling the zero coupon yield curve: a regression approach. Global Conference of Actuaries, 12.