Rebecca C. Steorts received her B.S. in Mathematics in 2005 from Davidson College, her MS in Mathematical Sciences in 2007 from Clemson University, and her PhD in 2012 from the Department of Statistics at the University of Florida under the supervision of Malay Ghosh, where she was a U.S. Census Dissertation Fellow and was a recepient for Honorable Mention (second place) for the 2012 Leonard J. Savage Thesis Award in Applied Methodology. Rebecca was a Visiting Assistant Professor in 2012--2015, where she worked closely with Stephen E. Fienberg.
Rebecca is currently an Associate Professor (with tenure) in the Department of Statistical Science at Duke University. She was an Assistant Professor at Duke University from 2015 -- 2022. She is affliated faculty in the Departments of Computer Science and Biostatics and Bioinformatics, the information intiative at Duke (iiD), and the Social Science Research Institute. Rebecca is also a full time employee with the United State Census Bureau (as of May 2022), where she is a Mathematical Statistician within the Center for Survey Research Methodology. She previously was a Special A Employee of the Bureau (December 2017 -- May 2022) and previously a contractor for the Bureau, working on record linkage projects.
Rebecca was named to MIT Technology Review's 35 Innovators Under 35 for 2015 as a humantarian in the field of software. Her work was profiled in the Septmember/October issue of MIT Technology Review and she was recognized with an invited talk at EmTech in November 2015. In addition, Rebecca is a recepient of a NSF CAREER award, a collaborative NSF award, a collaborative grant with the Laboratory of Analaytic (LAS) at NC State University, a Metaknowledge Network Templeton Foundation Grant, the University of Florida (UF) Graduate Alumni Fellowship Award, the U.S. Census Bureau Dissertation Fellowship Award, and the UF Innovation through Institutional Integration Program (I-Cubed) and NSF for development of an introductory Bayesian course for undergraduates. Her research interests are in large scale clustering, record linkage (entity resolution or de-duplication), privacy, network analysis, and machine learning for computational social science applications.