I am an incoming Assistant Professor at the Hobby School of Public Affairs (University of Houston) and I hold a PhD in Political Science and MA in Statistics from Rice University. I also hold an MA in American Studies (with a focus on African American History and Culture) from Doshisha University. My current email address is Yuki.Atsusaka@dartmouth.edu.
In 2022-2023, I serve as a Guarini Dean’s Fellow in the Politics of Race and Ethnicity at Dartmouth College. In my research, I develop and apply new quantitative methods to study the effects of electoral engineering on minority representation in racially and ethnically diverse democracies. My dissertation titled Political Methodologies for Electoral Engineering and Minority Representation received the John W. Gardner Award for the Best Dissertation in Social Sciences. My Curriculum Vitae is available here.
2. Atsusaka, Yuki and Randolph T. Stevenson. 2021. “A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents“ Political Analysis [R Package: cWise] [Article Summary] [Replication Files] [Preprint]
1. Atsusaka, Yuki. 2021. “A Logical Model for Predicting Minority Representation: Application to Redistricting and Voting Rights Cases“ American Political Science Review 115 (4), 1210-1225. [R Package: logical] [Article Summary] [Replication Files]
- “Causal Inference with Ranking Data: Application to Survey Experiments and Electoral Engineering”
- “Does Ranked-Choice Voting Reduce Racial Polarization? Evidence from Agent-Based Modeling and Bay Area Mayoral Elections“ (with Theo Landsman) [Media Coverage] —A winner of New America’s Electoral Reform Research Group Grant
- “Theoretical Foundations for Evaluating Minority Representation“
- “Statistical Methods for Partially Ranked Ballot Data”
- “An Ecological Inference Model with Rank Data for Social Sciences” (with Thomas Weighill)
- “Ranked Preferences over National Policy, Pork Barrel, and Casework by Race” (with Matthew Hayes)
- “A Unified Theory of the Effect of Vote-by-Mail on Turnout“ (with Robert M. Stein)
- cWise: A (Cross)Wise Method to Analyze Sensitive Survey Questions
- logical: Computing and Visualizing Quantitative Predictions of Logical Models
As an instructor, I have taught the Math Prep (aka Math Prefresher) for the first-year graduate students in 2018-2019, Social Analysis and Simulation in R for the second-year graduate students in 2019, and Ecological Inference in Advanced Political Methodology in 2019. In addition, I have worked as a teaching assistant for graduate courses such as Causal Inference (Fall 2019), Advanced Maximum Likelihood Estimation (Spring 2019), and Machine Learning/Computational Social Science (Fall 2020) and undergraduate classes such as Applied Research Methods (2021-2022). I have also founded and organized the Rice Methodology Research Group in the Department of Political Science.