I graduated from Duke University with a BS in mathematics in 1992. After working for two years as an
actuary, I got my Ph.D. in statistics from Harvard University in 1999. I landed back at Duke in the Department of Statistical
Science in Fall 2002. Between 2010 and 2015, I was the Mrs. Alexander Hehmeyer Professor of Statistical Science, having been
appointed as a Bass Chair in recognition of "excellence in undergraduate teaching and research."
I am honored to be the recipient of the Alumni Distinguished Undergraduate Teaching
Award for 2007 (news story
about the award). This annual award is given by Duke undergraduates to a member of the Duke faculty.

I participate in both applied and methodological research in statistical science. I am most interested in applications involving social science and public policy, although I enjoy working with researchers in all disciplines. My methodological research focuses mainly on statistical methods for protecting data confidentiality, for handling missing data, and for modeling complex data including methods for causal inferences. In 2015,* The Atlantic* published
a story about my research on methods for protecting data confidentiality. In 2009, Duke ran an article on my research interests that
does a great job explaining some of the things that intrigue me. I am the Principal Investigator of the Triangle Census Research
Network, which is
funded by the National Science Foundation to improve the practice of data dissemination among federal statistical agencies. I also am the Deputy Director of the
Information Initiative at Duke, an institute dedicated to research and applications in the analysis of large-scale (and not large-scale) data.

I participate in both applied and methodological research in statistical science. I am most interested in applications involving social science and public policy, although I enjoy working with researchers in all disciplines. My methodological research focuses mainly on statistical methods for protecting data confidentiality, for handling missing data, and for modeling complex data including methods for causal inferences. In 2015,