Assistant Professor of Political Science at Columbia University specializing in tailored experiments, public opinion research, and political psychology.
I am an Assistant Professor of Political Science at Columbia University, specializing in political psychology with a focus on attitude and belief change. My research examines how the quality of political information influences political engagement and representation, paying particular attention to the impact of misinformation within immigrant communities.
My most recent work examines the potential for generative artificial intelligence to improve our understanding of public opinion, leveraging personalized surveys that adaptively respond to voters. I have been developing LLM-powered methodology since 2021, beginning with GPT-2 for placebo generation and expanding into adaptive surveys, tailored experiments, and retrieval-augmented tools. My work has been published in the American Political Science Review, American Journal of Political Science, Journal of Politics, British Journal of Political Science, PNAS, and Political Analysis.
Discussed applications of large language models to experimental design, including tailored experiments and tools for mapping the latent network of beliefs and attitudes.
Presented at this event hosted by the NYU Abu Dhabi Institute in New York, discussing the intersection of data science, artificial intelligence, and political research.
Gave a talk titled "Generative AI and Respondent-Centered Social Science," discussing applications of Generative AI to survey design and randomized experiments.
Presented Crowdsourced Adaptive Surveys, exploring how large language models and multi-arm bandit algorithms can optimize survey questions in real-time based on participant feedback.
Delivered a presentation to the New York Chapter of the American Association for Public Opinion Research on the innovative use of Generative AI in surveys and experiments. Discussed the potential and challenges of using LLMs for adaptive polling and tailored experimental designs.
AI-generated ads tailored to voters' issue priorities outperform demographic and personality-based microtargeting in audio-based conjoints.
A critical assessment of attitude polarization using tailored experiments.
Download PaperExploring the impact of deeply-held issues among Latinos using personalized conjoint experiments.
Real-time survey optimization using multi-arm bandits. Open-source research implementation available.
Developing dynamic measures of responsiveness through retrieval augmented generation.
Using language models to generate placebo text for survey experiments, reducing researcher degrees of freedom.
Using LLMs and retrieval-augmented generation to create personalized voting aid applications.
Download PaperA web application for generating tailored experimental designs in Qualtrics.
Generate Tailored ExperimentA tutorial on how to use AI to ask follow-up questions in Qualtrics.
See TutorialA Qualtrics plugin that verifies physical human presence to combat AI survey fraud.
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