Hane Lee
Hi! I am a final year PhD candidate in the Statistics Department at Columbia University, advised by Michael Sobel. My first name is pronounced [Hayn], and my pronouns are they/them.
My research focuses on the intersection of statistics and political science, motivated by substantive questions from American politics–especially in public opinion, political polarization, and race, ethnicity, and politics. To operationalize and measure political phenomena, I apply diverse statistical methods: optimal transport, latent factor models, network theory, optimization, causal inference, and more. The topics I have been working on include developing new measures of ideological and affective polarization using the Wasserstein distance and measuring congressional social ties from roll call votes. I am also interested in studying racial methodologies from a critical theory perspective.
Before coming to Columbia, I was at MIT Media Lab’s Opera of the Future group for an MS where I designed interactive AR musical experiences and helped produce hybrid acoustic+digital musical performances. I also received a BS from MIT in Electrical Engineering with a minor in Music.
News
[May 2024] I will be presenting my paper on the Wasserstein Bipolarization Index at PolMeth 2024.
[May 2023] I will be presenting a poster on the fused latent factor and graphical modeling of roll-call votes at PolMeth 2023.
Research
Preprint. Hane Lee and Michael Sobel (2024). “Measuring Public Opinion: “The Wasserstein Bipolarization Index”, with Application to Cross-National Attitudes Toward Mandatory Vaccination for COVID-19.”
Abstract
Although the topic of opinion polarization receives much attention from the media, public opinion researchers and political scientists, the phenomenon itself has not been adequately characterized in either the lay or academic literature. To study opinion polarization among the public, researchers compare the distributions of respondents to survey questions or track the distribution of responses to a question over time using ad-hoc methods and measures such as visual comparisons, variances, and bimodality coefficients. To remedy this situation, we build on the axiomatic approach in the economics literature on income bipolarization, specifying key properties a measure of bipolarization should satisfy: in particular, it should increase as the distribution spreads away from a center toward the poles and/or as clustering below or above this center increases. We then show that measures of bipolarization used in public opinion research fail to satisfy one or more of these axioms. Next, we propose a p-Wasserstein polarization index that satisfies the axioms we set forth. Our index measures the dissimilarity between an observed distribution and a distribution with all the mass clustered on the lower and upper endpoints of the scale. We use our index to examine bipolarization in attitudes toward governmental COVID-19 vaccine mandates across 11 countries, finding the U.S and U.K are most polarized, China, France and India the least polarized, while the others (Brazil, Australia, Columbia, Canada, Italy, Spain) occupy an intermediate position.
Working paper. Hane Lee, Andrew Davison, and Zhiliang Ying. “Measuring Social Ties from Roll Call Votes: A Fused Latent Factor and Social Network Approach”.
Abstract
Congressional literature suggests that the motivations behind roll call votes are complex, spanning the legislator's ideology, party strategies, and social influences. In terms of methodology, latent factor models have dominated roll call analysis, where the estimated ``ideal points'' are interpreted as the legislators' partisan-ideological positions, but these models do not account for partisan or social motivations behind the votes. On the other hand, some researchers have explored the social influence behind these votes using network models, but this approach often overlooks the role of ideology or parties. We address this gap by integrating the partisan-ideological and social approaches through a fused latent factor and social network model. This model decomposes the effects of partisan-ideology and social connections on roll call votes while giving priority to the former. Additionally, our method provides a direct measurement of social ties from roll call votes, rather than relying on proxies such as cosponsorship to first estimate the social effect and later make connections to political outcomes. We apply our model to the 101st Senate and find that the model successfully decomposes ideology and partisanship from social ties. The estimated social network captures notable friendships and geographical communities. We also demonstrate that cosponsorship and shared committee membership, commonly viewed as indicators of social connections, are either closely aligned with the legislator's revealed partisan-ideological preferences or have minimal legislative impact.
Working paper. Yuki Atsusaka, Diana Da In Lee, and Hane Lee. “The Racial Margin of Victory for Minority Candidates Emergence”.
Abstract
Coming soon!
Chris Andrade, Jonathan Auerbach, Icaro Bacelar, Hane Lee, Angela Tan, Mariana Vazquez, and Owen Ward (2023). “Does it pay to park in front of a fire hydrant?”. Significance 20(1), pp. 28–30.
Teaching
Instructor (at Columbia University)
- Calculus-based Introduction to Statistics (Summer 2024) [Syllabus]
Teaching Assistant (at Columbia University)
Graduate
- Probability Theory (Fall 2021, Spring 2021, Fall 2020)
- Statistical Inference (Fall 2023)
- Accelerated Probability Theory/Statistical Inference (Fall 2022, Spring 2023, Fall 2024)
- Statistical Machine Learning (Spring 2022)
- Linear Regression Models (Spring 2024)
- Bayesian Statistics (Summer 2022)
Undergraduate
- Introduction to Statistics (Spring 2020)
- Introduction to Statistical Reasoning (Fall 2019)
Tutor (at MIT HKN and Math Learning Center)
Undergraduate
- Signals and Systems (6.003)
- Probability and Random Variables (18.600)
- Linear Algebra (18.06)
- Differential Equations (18.03)