Jeremy Seeman

Data Science Fellow Alum, Michigan Institute for Data Science

Research Associate, Urban Institute; Adjunct Research Assistant Professor, ICPSR, University of Michigan

I’m broadly interested in understanding how public data curators embed socially desirable values like privacy and confidentiality protections, equity, and reproducibility into their data publishing practices. My methodological research combines tools from theoretical computer science and computational social science to design and characterize complex structured errors induced by these practices. In doing so, I aim to demonstrate how these data curator interventions affect reproducible social science and evidence-based policymaking. Additionally, my qualitative research investigates the sociological and normative dimensions of how these interventions are implemented in practice; in particular, I’m interested in translational gaps between formal mathematical approaches and sociological approaches to ethics and values in data publishing, especially as applied to law and policy. My work at MIDAS continues this research in collaboration with the Inter-university Consortium for Political and Social Research (ICPSR), here at the Institute for Social Research (ISR).

• Science Mentor: Yajuan Si, Institute for Social Research
• Research Theme: Refining formal privacy methods and applying them to survey data.