Rebecca Carter Steorts
Associate Professor of Statistical Science
You can find more information about my research group and work at:
https://resteorts.github.io/
Recent papers of mine can be found at
https://arxiv.org/search/?query=steorts&searchtype=all&source=header
https://resteorts.github.io/
Recent papers of mine can be found at
https://arxiv.org/search/?query=steorts&searchtype=all&source=header
Current Appointments & Affiliations
- Associate Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2022
- Assistant Professor of Biostatistics and Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2015
- Associate Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2022
Contact Information
- Background
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Education, Training, & Certifications
- Ph.D., University of Florida 2012
- M.S., Clemson University 2007
- B.S., Davidson College 2005
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Previous Appointments & Affiliations
- Assistant Professor of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2015 - 2022
- Assistant Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2016 - 2022
- Scholar In Residence in the Department of Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2015
- Recognition
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In the News
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OCT 30, 2018 Duke Research Blog -
SEP 17, 2018 -
MAR 16, 2016 -
OCT 1, 2015
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- Expertise
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Global Scholarship
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Research
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- Research
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Selected Grants
- Foreign-Born Scientists and Engineers and the U.S. Workforce (FBSE) awarded by Research Triangle Institute International 2022 - 2024
- HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms awarded by National Science Foundation 2019 - 2023
- Unsupervised Detection of Bias in Record Linkage awarded by Google Inc. 2021 - 2023
- CAREER: Scalable Record Linkage through the Microclustering Property awarded by National Science Foundation 2017 - 2023
- Interpretable and Scalable Entity Resolution Applied to the Decennial Census awarded by Alfred P. Sloan Foundation 2019 - 2022
- Posterior Prototyping: Bridging the gap between Record Linkage and Regression awarded by North Carolina State University 2019
- Collaborative Research: Record Linkage and Privacy-Preserving Methods for Big Data awarded by National Science Foundation 2015 - 2018
- LAS DO6: Theory and Methods for Coarsened Decision Making; Synthetic Data Release: The Tradeoff between Privacy and Utility of Big Data awarded by North Carolina State University 2016
- Metaknowledge Network: Knowledge about Knowledge to Answer the Big Questions awarded by University of Chicago 2015 - 2016
- Metaknowledge Network: Knowledge about Knowledge to Answer the Big Questions awarded by University of Chicago 2015 - 2016
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External Relationships
- US Census Bureau
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Bedoya, Armando D., Meredith E. Clement, Matthew Phelan, Rebecca C. Steorts, Cara O’Brien, and Benjamin A. Goldstein. “Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient Deterioration.” Crit Care Med 47, no. 1 (January 2019): 49–55. https://doi.org/10.1097/CCM.0000000000003439.Full Text Link to Item
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Chen, B., A. Shrivastava, and R. C. Steorts. “Unique entity estimation with application to the syrian conflict.” Annals of Applied Statistics 12, no. 2 (June 1, 2018): 1039–67. https://doi.org/10.1214/18-AOAS1163.Full Text
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Durante, D., N. Mukherjee, and R. C. Steorts. “Bayesian learning of dynamic multilayer networks.” Journal of Machine Learning Research 18 (April 1, 2017): 1–29.
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Steorts, R. C., R. Hall, and S. E. Fienberg. “A Bayesian Approach to Graphical Record Linkage and Deduplication.” Journal of the American Statistical Association 111, no. 516 (October 1, 2016): 1660–72. https://doi.org/10.1080/01621459.2015.1105807.Full Text Open Access Copy
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Miller, Jeffrey, Brenda Betancourt, Abbas Zaidi, Hanna Wallach, and Rebecca C. Steorts. “Microclustering: When the Cluster Sizes Grow Sublinearly with the Size of the Data Set,” December 2, 2015.Open Access Copy Link to Item
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Wehbe, L., A. Ramdas, R. C. Steorts, and C. R. Shalizi. “Regularized brain reading with shrinkage and smoothing.” Annals of Applied Statistics 9, no. 4 (December 1, 2015): 1997–2022. https://doi.org/10.1214/15-AOAS837.Full Text
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Sadosky, Peter, Anshumali Shrivastava, Megan Price, and Rebecca C. Steorts. “Blocking Methods Applied to Casualty Records from the Syrian Conflict,” October 26, 2015.Link to Item
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Steorts, R. C. “Entity resolution with empirically motivated priors.” Bayesian Analysis 10, no. 4 (January 1, 2015): 849–75. https://doi.org/10.1214/15-BA965SI.Full Text
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Steorts, R. C., and M. D. Ugarte. “Comments on: “Single and two-stage cross-sectional and time series benchmarking procedures for small area estimation”.” Test 23, no. 4 (December 1, 2014): 680–85. https://doi.org/10.1007/s11749-014-0386-2.Full Text
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Steorts, Rebecca C. “Smoothing, Clustering, and Benchmarking for Small Area Estimation,” October 26, 2014.Link to Item
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Broderick, Tamara, and Rebecca C. Steorts. “Variational Bayes for Merging Noisy Databases,” October 17, 2014.Open Access Copy Link to Item
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Steorts, Rebecca C., and M Delores Ugarte. “Discussion of "Single and Two-Stage Cross-Sectional and Time Series Benchmarking Procedures for SAE",” May 25, 2014.Link to Item
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Fienberg, S. E., and R. C. Steorts. “Discussion of "Estimating the distribution of dietary consumption patterns".” Statistical Science 29, no. 1 (January 1, 2014): 95–96. https://doi.org/10.1214/13-STS448.Full Text
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Steorts, R. C., R. Hall, and S. E. Fienberg. “SMERED: A Bayesian approach to graphical record linkage and de-duplication.” Journal of Machine Learning Research 33 (January 1, 2014): 922–30.Open Access Copy
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Steorts, R. C., S. L. Ventura, M. Sadinle, and S. E. Fienberg. “A comparison of blocking methods for record linkage.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8744 (January 1, 2014): 253–68. https://doi.org/10.1007/978-3-319-11257-2_20.Full Text
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Ghosh, M., and R. C. Steorts. “Two-stage benchmarking as applied to small area estimation.” Test 22, no. 4 (November 1, 2013): 670–87. https://doi.org/10.1007/s11749-013-0338-2.Full Text
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Pane, Michael A., Samuel L. Ventura, Rebecca C. Steorts, and A. C. Thomas. “Trouble With The Curve: Improving MLB Pitch Classification,” April 5, 2013.Link to Item
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Steorts, Rebecca, and Malay Ghosh. “On Estimation of Mean Squared Errors of Benchmarked Empirical Bayes Estimators.” Statistica Sinica, 2013. https://doi.org/10.5705/ss.2012.053.Full Text
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Datta, G. S., M. Ghosh, R. Steorts, and J. Maples. “Bayesian benchmarking with applications to small area estimation.” Test 20, no. 3 (November 1, 2011): 574–88. https://doi.org/10.1007/s11749-010-0218-y.Full Text
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Conference Papers
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Steorts, R. C., A. Tancredi, and B. Liseo. “Generalized bayesian record linkage and regression with exact error propagation.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11126 LNCS:297–313, 2018. https://doi.org/10.1007/978-3-319-99771-1_20.Full Text
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Steorts, R. C., and A. Shrivastava. “Probabilistic blocking with an application to the syrian conflict.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11126 LNCS:314–27, 2018. https://doi.org/10.1007/978-3-319-99771-1_21.Full Text
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Zanella, G., B. Betancourt, H. Wallach, J. Miller, A. Zaidi, and R. C. Steorts. “Flexible models for microclustering with application to entity resolution.” In Advances in Neural Information Processing Systems, 1425–33, 2016.
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- Teaching & Mentoring
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Recent Courses
- STA 602L: Bayesian Statistical Modeling and Data Analysis 2023
- STA 360L: Bayesian Inference and Modern Statistical Methods 2022
- STA 391: Independent Study 2022
- STA 493: Research Independent Study 2022
- STA 602L: Bayesian Statistical Modeling and Data Analysis 2022
- STA 995: Internship 2022
- COMPSCI 394: Research Independent Study 2021
- STA 360L: Bayesian Inference and Modern Statistical Methods 2021
- STA 490: Special Topics in Statistics 2021
- STA 690: Special Topics in Statistics 2021
- STA 993: Independent Study 2021
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