Jeremy Seeman is a Michigan Data Science Fellow at the Michigan Institute for Data Science (MIDAS) at the University of Michigan. He has joint appointments in the Institute for Social Research (ISR) and the Center for Ethics, Society, and Computing (ESC). His research focuses on statistical data privacy, quantitative methods in the social sciences, and social values in data governance. His work spans multiple disciplines, with publications, invited presentations, and collaborations in statistics, machine learning, social sciences, and law. He is the recipient of the U.S Census Bureau Dissertation Fellowship and the ASA Pride Scholarship. Prior to joining UMich, Jeremy completed his PhD in statistics at Penn State and his BS in Physics and MS in Statistics at the University of Chicago, where he was a research fellow at the Center for Data Science and Public Policy.