Srijan Sengupta

A generalized hypothesis test for community structure in networks

Yanchenko, E., & Sengupta, S. (2024, March 11). NETWORK SCIENCE, Vol. 3.

Word Embeddings as Statistical Estimators

Dey, N., Singer, M., Williams, J. P., & Sengupta, S. (2024, May 9). SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS, Vol. 5.

A natural language processing approach to categorise contributing factors from patient safety event reports

BMJ Health & Care Informatics, 30(1), e100731.

Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing

DIAGNOSTICS, 13(7).

Core-periphery structure in networks: A statistical exposition

Statistics Surveys, 17(none), 42–74.

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.

How AI can Help us Understand and Mitigate Error Propagation in Radiation Oncology

In Artificial Intelligence in Radiation Oncology (pp. 305–334).

Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology

Larsen, N., Stallrich, J., Sengupta, S., Deng, A., Kohavi, R., & Stevens, N. T. (2023, October 17). AMERICAN STATISTICIAN, Vol. 10.

Wildfire Pollution Emissions, Exposure, and Human Health: A Growing Air Quality Control Issue

Ali, M. S., Aneja, V., Ganguly, I., Sanyal, S., & Sengupta, S. (2023, November 8).

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.

Scalable Community Extraction of Text Networks for Automated Grouping in Medical Databases

Journal of Data Science, 1–20.

Using Community Detection Techniques to Identify Themes in COVID-19–Related Patient Safety Event Reports

Journal of Patient Safety, 18(8), e1196–e1202.

Wildfire Pollution Exposure and Human Health: A Growing Air Quality and Public Health Issue

Environmental Science Proceedings, 19(1), 59.

Broader impacts of network monitoring: Its role in government, industry, technology, and beyond

Stevens, N. T., Wilson, J. D., Driscoll, A. R., McCulloh, I., Michailidis, G., Paris, C., … Sparks, R. (2021, September 9). QUALITY ENGINEERING, Vol. 33.

Foundations of network monitoring: Definitions and applications

Quality Engineering, 33(4), 719–730.

Online Social Deception and Its Countermeasures: A Survey

IEEE Access, 9, 1770–1806.

Research in network monitoring: Connections with SPM and new directions

Quality Engineering, 33(4), 736–748.

Scalable Estimation of Epidemic Thresholds via Node Sampling

Sankhya A, 84(1), 321–344.

The Impact of COVID-19 on Medical Device Reporting and Investigation

Patient Safety, 9, 28–35.

The interdisciplinary nature of network monitoring: Advantages and disadvantages

Quality Engineering, 33(4), 731–735.

Online Social Deception and Its Countermeasures: A Survey

IEEE Access.

Scalable estimation of epidemic thresholds via node sampling

ArXiv Preprint ArXiv:2007.14820.

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.

A Bootstrap-Based Approach for Improving Measurements by Retarding Potential Analyzers

Journal of Geophysical Research: Space Physics, 124(6), 4569–4584.

A Bootstrap-based Inference Framework for Testing Similarity of Paired Networks

ArXiv Preprint ArXiv:1911.06869.

Parameter estimation from retarding potential analyzers in the presence of realistic noise

Virginia Tech.

Statistical evaluation of spectral methods for anomaly detection in static networks

Network Science, 7(3), 319–352.

Toward epidemic thresholds on temporal networks: a review and open questions

Applied Network Science, 4(1), 1–21.

Using artificial neural networks to predict pH, ammonia, and volatile fatty acid concentrations in the rumen

Journal of Dairy Science, 102(10), 8850–8861.

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.

A block model for node popularity in networks with community structure Series B Statistical methodology

Anomaly detection in static networks using egonets

ArXiv Preprint ArXiv:1807.08925.

Discussion of “Statistical methods for network surveillance”

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

Quality and Reliability Engineering International, 34(8), 1699–1716.

Performance evaluation of social network anomaly detection using a moving window-based scan method

Quality and Reliability Engineering International, 34(8), 1699–1716.

The effect of temporal aggregation level in social network monitoring

PLOS ONE, 13(12), e0209075.

The effect of temporal aggregation level in social network monitoring

Plos One, 13(12), e0209075.

A subsampled double bootstrap for massive data

Journal of the American Statistical Association, 111(515), 1222–1232.

Statistical analysis of networks with community structure and bootstrap methods for big data

University of Illinois at Urbana-Champaign.

Recent developments in bootstrap methods for dependent data

Journal of Time Series Analysis, 36(3), 462–480.

Spectral clustering in heterogeneous networks

Statistica Sinica, 1081–1106.

The dependent random weighting

Journal of Time Series Analysis, 36(3), 315–326.

Modeling the zero coupon yield curve: a regression approach

Global Conference of Actuaries, 12.