I am a Political Science PhD Student and a Research Associate at the Center of the Politics of Development at UC Berkeley.
My research focuses on belief formation and political accountability with a focus on Sub-Saharan Africa and Latin America. I combine insights from the field of political behavior and formal theory to understand how political beliefs, attitudes, and electoral choice are affected by information and social signaling.
I am also commited to reproducibility and research transparency. Some platforms I helped create for that purpose include the DDWizard, a web app for defining and inspecting research designs, and the Metaketa I Meta-Analysis dashboard for real-time sensitivity analysis of research results.
You can find a copy of my CV here.
 2019. “Meta-Analysis” (with Thad Dunning, Anirvan Chowdhury, Guy Grossman, Macartan Humphreys, Susan D. Hyde, Craig Mcintosh, and Gareth Nellis) in Information, Accountability, and Cumulative Learning: Lessons from Metaketa I. Thad Dunning, Guy Grossman, Macartan Humphreys, Susan D. Hyde, and Craig Mcintosh (Eds.). Cambridge University Press.
Book chapter | Replication Code
 2019. “Voter information campaigns and political accountability: Cumulative findings from a preregistered meta-analysis of coordinated trials.” (with Thad Dunning et al.) Science Advances 5.7 (2019): eaaw2612.
Article | Replication Code
“Experimental Evidence from Uganda, South Africa, and the United States on Ethnic Identification and Ethnic Deception” (with Adam Harris, Daniel Nielson, Lily Medina, Michael Findley, Jeremy Weinstein, James Habyarimana, Macartan Humphreys, and Daniel Posner).
 2019. DesignLibrary: Library of Research Designs (with Graeme Blair, Jasper Cooper, Alexander Coppock, Macartan Humphreys, Neal Fultz, and Lily Medina). R package version 0.1.4, URL https://cran.r-project.org/web/packages/DesignLibrary/index.html
DDWizard (with Sisi Huang and
Markus Konrad). This web app allows users to select and customize research designs from a library of templates, simulate data, and obtain diagnostic statistics such as power, bias, and root mean squared error of estimates. The app is an interface for using the
DeclareDesign framework and R packages that does not require knowledge of R and allows easy sharing of designs.
Read more | Github repo