Tyler Simko
Scholar

Tyler Simko

Google Scholar ID: M75v7LIAAAAJ
University of Michigan
state and local politicscomputational social science
Citations & Impact
All-time
Citations
461
 
H-index
10
 
i10-index
10
 
Publications
20
 
Co-authors
4
list available
Resume (English only)
Academic Achievements
  • Published papers in Nature: Scientific Data, Proceedings of the 2025 AAAI Conference on Artificial Intelligence, PNAS, and Nature: Scientific Reports. Received APSA Best Paper Award in Education Politics, Honorable Mention for APSA Best Paper Award in Urban and Local Politics, and Robert H. Durr Award (MPSA). Research covered by The New York Times, The Washington Post, ABC News, and The Associated Press.
Research Experience
  • Active research agendas in legislative redistricting ("gerrymandering"), subnational policymaking by governments like city councils and school boards, school segregation, and data privacy. Develops and uses techniques in computational social science, causal inference, and machine learning to evaluate inequality and how it can be reduced. Partners with federal, state, and local officials to improve program design and reduce administrative burdens. Served as a Data Scientist at the Office of Evaluation Sciences, working in an interdisciplinary team to design causal evaluations of government programs.
Education
  • Received a PhD from the Department of Government at Harvard University in 2024, affiliated with the Center for American Political Studies, Program on Education Policy and Governance, and the Harvard Mellon Urban Initiative. Previously, was a Postdoctoral Research Associate at Princeton University in the Department of Politics.
Background
  • Specializes in US state and local politics, political geography, and computational social science. Assistant Professor of Political Science at the University of Michigan. Co-PI of the Algorithm-Assisted Redistricting Methodology (ALARM) Project, which focuses on how public policy influences spatial inequality.
Miscellany
  • Co-created LocalView, the largest audio, video, and text database of local government meetings in the United States, with Soubhik Barari. Elected to two terms on the South Amboy before graduate school.