I’m delighted to be an Omidyar Postdoctoral Fellow at the Santa Fe Institute.
Within my research, I am motivated by careful, patient, and theoretically informed data science. I hope that this approach to method development will lead to important insights about both our society and the ways in which we study it. I hope that engaging in this challenge reveals a cycle wherein data scientists learn from the social sciences so that we may develop more precise technical methodology which we can use to learn more about the social sciences.
I see this motivating perspective as broadly applicable throughout data science, but I specifically apply it to my work on developing methodology for studying perceived social networks. My research agenda centers around identifying and developing theories for understanding what I refer to as social-cognitive structure: the interplay of how social structures are perceived in a community and how these perceptions are patterned across individuals. To develop a study of social-cognitive structure in network perceptions my research program focuses on (i) developing novel mathematical tools and numerical methods for identifying structure in data, (ii) innovating survey methods for collecting network perceptions, and (iii) developing and connecting existing sociological theories to explain social-cognitive structure.
Click on one of the links on the left to learn more about me and what I’m up to.
