I am a PhD graduate from NYU's Wilf Department of Politics and current Postdoctoral Fellow at Vanderbilt's Data Science Institute with 10+ years of wide-ranging research experience in both methods and applications. I specialize in computational methods --with emphasis on NLP, networks and machine learning-- and their application to the study of political semantics and attitude judgments. My research draws in significant ways from multiple disciplines --cognitive science, computer science, linguistics and social psychology-- and has been published (or is forthcoming) in the Journal of Politics, the British Journal of Politics and the Journal of Labor Economics among others. In 2020 I launched Weavio LLC to help researchers collect and extract robust actionable insights from qualitative data at scale using NLP and AI.
"Embedding Regression: Models for Context-Specific Description and Inference" (with Spirling, A. and Stewart, B.), under peer review. (link).
"Word Embeddings: What works, what doesn't, and how to tell the difference for applied research" (with Spirling, A.), forthcoming in the Journal of Politics, 2021 (link).
Weavio Voice: App to build short voice surveys with automated transcription, sentiment and keyword extraction (with Ghobashy, M. and Elaraby, M.).
Weavio Qual-AI (demo): Semantic search engine for coding open-ended responses and feedback (with Ghobashy, M.).
conText: An R package for regression-based inferences using text data (with Spirling, A. and Brandon, S.).
dyadRobust: Cluster-robust standard errors for dyadic data (with Bisbee, J.).
shinyGeNNs: R Shiny App to collect semantic fluency data.
turingTT: R Shiny App to perform Turing-style evaluations of word embedding models (as laid out in Rodríguez, P. And Spirling, A., 2021).