The abiding concern of my research is an updated version of a classic sociological question: is the purpose of quantitative knowledge to understand the world, or to change it? My current work is divided into three primary areas:
Governance of Artificial Intelligence in Healthcare
Much of my current work concerns the social classification of AI models and the ethical implications for US healthcare. Based on interviews with data scientists, clinicians, and legal experts, this research asks: how is AI governance being envisioned in the absence of an established regulatory framework? In an article forthcoming at BMC Health Services Research, my coauthors and I theorize the ethical oversight of AI as a “responsibility vacuum”: most AI models are not classified as medical devices or human subjects research, and therefore they escape the kind of scrutiny usually applied to health information technologies. Additional work related to this project has been published in the American Journal of Bioethics and will appear in a forthcoming editorial at Health Affairs Forefront.
At NYU Langone, I am also engaged in more practical, team-based work in this area as part of the institution's Responsible AI initiative. This collaborative effort prompted me to begin work on a book project, How Management Made Medicine: The Evolution of 'Quality Improvement' from Industrial Production to Medical AI, which examines the historical context that led to AI models being mostly classified as 'quality improvement' (QI) rather than clinical research projects, which has ethical oversight implications. QI techniques have a long history that can be traced through statistical methods developed by early 20th century management scientists, the post-World War Two Japanese economic miracle, and late-20th century U.S. corporate innovation before this paradigm became relevant for healthcare practitioners. Today, the ethics and practices of QI work is hotly debated in academic medicine, which I have written about in the American Journal of Bioethics. My archival research for this project has received support from the History and Political Economy Project and the Consortium for the History of Science, Technology, and Medicine, where I am a 2025-2026 Research Fellow.
The Sociology of Economic and Statistical Expertise
Quality improvement techniques in healthcare originally were developed by management scientists experimenting with statistical methods in the mid-twentieth century, which relates to my second major area of interest: the sociology of statistical and economic expertise. This work rethinks and reframes the enormously consequential economics of U.S. social policy as a predominantly reactive enterprise, in which existing social programs and data sources constrain economists' capacity to effect policy change. I find that when it comes to topics like healthcare or education, economics is not an unchanging monolith in policy settings, and that the further one gets from the field’s disciplinary core, economic theory is less essential to the work of economists than a common methodological language (which is not always legible to policy audiences). In the wake of the COVID-19 pandemic, this analytical approach has found economists joining the fray of experts investigating issues such as 'health equity,' a state of affairs which contrasts sharply with popular critiques of the field. Research related to this project has been published in Theory and Society, the Journal of Cultural Economy, Science, Technology, & Human Values, the Journal of Education Policy, and Economy and Society. Most recently, I wrote a piece for TIME Magazine's Made By History series based on this research.
Genetics and Uncertainty in Social Policy
A final project, Polygenic Prediction, turns a sociological lens on the production of new quantitative indicators in the field of behavior genetics that are beset by a host of uncertainties. While so-called polygenic scores have received critical attention primarily for their potentially eugenic implications in policy settings, this project investigates how uncertainty is inherent to research in this domain beginning with the collection of biobank data that disproportionately feature people with European ancestry, resulting in reference 'populations' that are not representative. My research on polygenic scores is a collaboration with UCLA's Aaron Panofsky and Nanibaa' Garrison that aims to empower clinicians, policymakers, and the public so that when polygenic prediction is applied, it occurs as ethically and equitably as possible.