Tyler Simko

Tyler Simko is an Assistant Professor of Political Science at the University of Michigan, specializing in US state and local politics, political geography, and computational social science. His research focuses on understanding and addressing inequality in American public policy through innovative methodological approaches.

Education:

• Ph.D. in Government, Harvard University (2024)
• A.B. in Politics, Princeton University

Simko's research examines state and local politics in the United States with particular focus on political geography and subnational policymaking. His active research agendas include legislative redistricting ("gerrymandering"), local public meetings, school segregation, affordable housing, and data privacy. Methodologically, he develops new techniques in computational social science and machine learning to evaluate subnational inequality and how it can be reduced. His work regularly involves partnerships with federal, state, and local officials to improve the design of public policy.

His recent publications demonstrate a strong focus on applying computational methods to address real-world policy challenges, particularly in school desegregation, redistricting, and local government transparency. His research often leverages large-scale data collection efforts, such as LocalView (the largest database of local government meetings in the US), to analyze patterns of political behavior and policy outcomes across different jurisdictions.

Awards and Recognition:

• APSA 2024-25 Best Paper in Education Politics and Policy Award
• APSA 2024-25 Best Paper in Urban and Local Politics, Honorable Mention
• MPSA 2024 Robert H. Durr Award for "the best paper applying quantitative methods to a substantive problem"
• Derek C. Bok Award for Excellence in Graduate Student Teaching of Undergraduates (2023)

Simko teaches graduate and undergraduate courses in American Politics and Political Methodology at the University of Michigan. His teaching experience spans multiple institutions, including Harvard University and Princeton University. He has designed innovative courses on US Local Policymaking, data science, and computational social science. As a Data Scientist at the Office of Evaluation Sciences, he partners with federal, state, and local officials to improve program design and reduce administrative burdens.

He is a co-PI of the Algorithm-Assisted Redistricting Methodology (ALARM) Project and co-creator of LocalView, the largest audio, video, and text database of local government meetings in the United States. These projects represent significant contributions to the field of computational social science and provide valuable resources for researchers studying local governance and policy-making.