I research the capabilities and limitations of predictive machine learning models in social services, with a focus on child and family policy. My goal is to contribute to the safe regulation of these rapidly proliferating tools. I am also enthusiastic about data science, administrative data, and social safety net research more broadly!
My focus is on predictive models in child and family policy. I have been developing expertise in child and family policy since my undergraduate years, through a self-designed undergraduate major in Human Development and Social Policy, an internship in public benefits and community resource navigation, two years as a research assistant at the child and family policy research organization Child Trends, and a job as committee assistant for the Health and Human Services Committee of the New Mexico House of Representatives.
Updates
October 2024: Hanzhang Ren and I scored first place in Part 2 of the PreFer data challenge! This competition used the same outcome as Part 1 (births and adoptions), but this time, participants used Dutch administrative register data. We are now working on a paper to explore how sample size and feature sets affect predictive accuracy, as well as a paper on differences in predictability across demographic subgroups. Huge thanks to Lisa Sivak and Gert Stulp for organizing the challenge and facilitating data access, and to Juan Carlos Perdomo for his advice on using Catboost.
September 2024: Hanzhang Ren and I scored first place in Part 1 of the PreFer data challenge, in which participants competed to predict childbirths and adoptions using Dutch survey data! We are now working on a paper to explore which strategies contributed most to predictive accuracy.
May 2024: Check out our paper introducing the "REFORMS" checklist of recommendations for conducting and communicating high-quality machine-learning-based science! Huge thanks to Sayash Kapoor and Arvind Narayanan for leading this effort. Sayash, Arvind, and I were interviewed about the work here.