Hello!

I am a Research Associate at the Urban Institute and an adjunct professor at the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan.

Bio:

Jeremy Seeman is a Research Associate in the Data Governance and Privacy group at the Urban Institute, an adjunct professor at the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan, and an affiliate faculty member of the Center for Ethics, Society, and Computing (ESC).

Jeremy’s research focuses on technical and policy solutions for responsible data sharing, with an emphasis on deploying and governing privacy-enhancing technologies to responsibly expand access to sensitive public good data sources. His work has been featured in numerous interdisciplinary venues including statistics, computer science, law, philosophy, and policy. Jeremy’s work develops privacy-ehnacing data sharing solutions for numerous public good organizations, including the National Science Foundation, U.S. Census Bureau, and the Bureau of Justice Statistics.

Before joining Urban, Seeman was a Michigan Data Science Fellow at the University of Michigan. He received his BS in physics and MS in statistics from the University of Chicago and his PhD in statistics from Penn State University, where he was a U.S. Census Bureau Dissertation Fellow.

Recent news:

• I joined the organizing committee for the 6th AAAI Workshop on Privacy-Preserving Artificial Intelligence.
• Our paper “Privately Answering Queries on Skewed Data via Per Record Differential Privacy” (w/ William Sexton, David Pujol, and Ashwin Machanavajjhala) has been accepted at International Conference on Very Large Data Bases (VLDB), describing new privacy-preserving methodolgoy for the U.S. Census Bureau’s County Business Patterns (CBP) dataset.
• I joined the brand new Data Governance and Privacy practice area at the Urban Institute!
• Our paper “Privacy’s Odd Couple: Privacy Law and Privacy Engineering on Inference and Information Recovery” (w/ Palak Jain and Daniel Susser) appeared at Privacy Law Scholar’s Conference
• My paper “Better Privacy Theorists for Better Data Stewards” is out now at Journal of Privacy and Confidentiality.
• I presented our paper “Differentially Private Population Quantity Estimates via Survey Weight Regularization” (w/ Yajuan Si and Jerome Reiter) at the NBER Workshop on Data Privacy Protection and the Conduct of Applied Research.
• Our paper “An Exploratory Meta-Analysis to Identify Outlying Behavior in the NIST Collaborative Research Cycle Archive” with (amazing) undergrad Dhruv Kapur was accepted to the NIST Collaborative Research Cycle Explanatory Workshop.