Foerster-Bernstein Fellow 2016-17 and 2017-18: Brenda Betancourt, PhD

Dr. Betancourt joined Duke University as a post-doctoral associate in September 2015 after completing her PhD in Statistics and Applied Mathematics at the University of California, Santa Cruz (UCSC). Her research has focused on the study of network patterns in individual financial trading networks and its implication on market microstructures. Specifically, she is developing computationally scalable approaches to social science applications, with an emphasis on entity resolution (record linkage) and network analysis in conjunction with applications to human rights, public policy issues, official statistics, and author disambiguation. The two main thrust areas of Dr. Betancourt’s research, record linkage and network analysis, rely on clustering techniques. Conventional clustering models presume that the goal is to divide the data into a small number of high-probability clusters that increase in size as the number of data increases. However, when clusters are unique individuals in a population or in the presence of sparse network data, it is not natural to assume that more data always eventually means bigger clusters in the data partition. Dr. Betancourt’s collaborative research with Dr. Rebecca Steorts (A&S, Statistical Science) will focus on developing a novel approach for partition models specially designed to address the “small clustering" problem commonly observed in record linkage and sparse networks applications. To date, Dr. Betancourt has published 1 article with 3 more currently submitted. She is a past recipient of an NSF Travel Award to XIII Latin American Congress of Probability and Mathematical Statistics, a Sury Initiative Mini-Grant on Global Finance and International Risk Management, and a Chancellor’s Fellowship at UCSC. She has given 7 invited presentations.

Mentor: Rebecca Steorts, PhD, Assistant Professor of Statistical Science

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Rebecca C. Steorts is currently an Assistant Professor (2015) at Duke University in the Department of Statistical Science with affiliations in the Social Science Research Institute and the information initiative at Duke. She received her BS 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. She is interested in scalable computational methods for social science applications. Her current works focuses on recovering high dimensional objects from degraded data and determining how to recover the underlying structure. Methods used for this are entity resolution, small area estimation, locality sensitive hashing, and privacy-preserving record linkage as applied to medical studies, fmri studies, human rights violations, and estimation of poverty rates in hard to reach domains. Her research was on record linkage and sparse clustering was recently funded by the John Templeton Foundation, MetaKnowledge Network Grants Awarded, and November 2014. She was recently named to MIT Technology Review's 35 Innovators Under 35 for 2015 as a humanitarian in the field of software. Her work was profiled in the September/October issue of MIT Technology Review and she was recognized at a special ceremony along with an invited talk at EmTech in November 2015.