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I have a new preprint out on SocArXiv: Model Diversity Over Model Size: Unanimous LLM Ensembles Correct Over-Classification in Survey Coding. It’s a deep dive into one finding flagged in the CatLLM methods paper — that unanimous multi-model ensembling corrects over-classification — asking which ensemble ingredients actually drive the gain and where the gain shows up.
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I have a new preprint out on SocArXiv: High Agreement, Different Stories: How LLM Classifiers Reshape Demographic Patterns in Survey Data. This is a substantially revised version of my earlier preprint, with new analyses centered on how high agreement rates can mask thematic and demographic divergence between LLM and human coders.
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I have a busy conference season ahead. Here is where I’ll be presenting and what I’ll be talking about.
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I have a new preprint out on SocArXiv: An Empirical Investigation into the Utility of Large Language Models in Open-Ended Survey Data Categorization.
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On October 29th, 2025, I had the privilege of presenting at the University of Washington on how large language models can augment social science research. The presentation focused on CatLLM, an open-source Python package I developed to address a common challenge in demographic and social science research: analyzing open-ended survey responses and complex data at scale.
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On July 1st, 2025, I had the opportunity to present joint research with Dr. William H. Dow, Professor of Health Policy and Management and Director of the Center on the Economics and Demography of Aging at UC Berkeley. Our presentation explored a fascinating contradiction in Hispanic aging: the Hispanic health paradox in cognitive outcomes.
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I presented shared work with Professor William Dow and Henry Dow, comparing sociodemographic characteristics of older Hispanic adults in U.S. immigrant populations with those in their countries of origin, using census microdata and the American Community Survey (publication pending).
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I had the opportunity to present our research on public school background and probability of enrollment in a graduate degree at the 2025 PSA conference.
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I recently presented my research at the Science Envoy “Ask a Science Envoy” event at HopMonk Tavern in Novato—my first time presenting at a brewery and to a non-academic audience. The experience taught me the importance of navigating sensitive topics like partisanship and belief in science, especially when audience members began debating the subject.
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I’m super grateful to the Dominican team of The Caribbean American Dementia and Aging Study (CADAS).
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In our upcoming paper, “Measuring and Modeling the Impact of Partisan Differences in Health Behaviors on COVID-19 Dynamics,” we use a three-group Susceptible-Infected-Recovered model to highlight the importance of incorporating partisan differences into models of disease transmission. In this blog post, I want to fully explain what is happening in the background for readers who may be interested in utilizing it themselves. For those users, we also built an R shiny app (soon to be published as well). The link to the shiny app will be: here.
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I’m super grateful to the Puerto Rican team of The Caribbean American Dementia and Aging Study (CADAS). We got a lot done and things are moving quickly! Looking forward to visiting again soon.
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I had the opportunity to present our research on public school background and probability of enrollment in a graduate degree at the 2024 CAIR conference.
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The impact of non-pharmaceutical interventions and protective health behaviors, such as the use of face masks and physical distancing, on COVID-19 dynamics is well-documented, but sub-group heterogeneities in the adoption of these behaviors remains understudied. In this paper, we describe partisan differences in the adoption of protective health behaviors, and model how these differences can impact the dynamics of COVID-19.
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I have been selected as a Science Envoy for Wonderfest: The Bay Area Beacon of Science.
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I have been selected as a Libby Fee Science Fellow by the Greater Good Science Center.
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In the world of data analysis, ensuring the accuracy and consistency of datasets is crucial, especially when dealing with entities like school names that may be spelled differently across various sources. This discrepancy can pose significant challenges when trying to match records from different datasets. Traditional methods of data cleaning may fall short in addressing these inconsistencies effectively.
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This first ever blog post is in honor of my recently passed little man, Charlie the cat, who for 12 years went together with me through thick and thin. He was the best cat anyone could’ve asked for, and no other will ever be able to replace him. I will miss you dearly, Charles, aka “Chester.” I love you always.
