Spring 2024, Teaching Assistant

Undergraduate

When we consider how to allocate resources fairly, we think about how to distribute resources across the different segments of society that exist right now.  Often, we talk about how much inequality we should tolerate and how resources should be distributed across rich and poor, rural and urban, high-income countries and the global South. This class focuses on the less studied question of when and how we distribute resources fairly across time and different generations. Specifically, we will discuss the following key questions: What do we owe to people in the future?  Should we care about people in the future?  When have humans cared about future generations?  What kinds of policies and practices can make current people care more about future people? To explore these questions, we will review work from the social and behavioral sciences, as well as from history, art, and philosophy.  We will look at academic research, policy reports, cases, insights, and texts from different times, places, and cultures.

Summer 2022, Teaching Fellow

Undergraduate

Examines the ethical context around students' summer internships, research, and other experiential learning activities. During the summer and through the first four weeks of the fall term, students engage in small group discussions of applied ethical practices and case studies. Throughout the subject, they explore their own moral values through a reflective final project that examines their engagement with ethics during their summer experience. Includes local field trips, practitioner interviews, and/or similar opportunities for interaction with professional ethics. Concludes with a showcase of final presentations.

Fall 2020, Teaching Assistant

Undergraduate

Empirical studies in political science is entering a new era of “Big Data” where a diverse range of data sources have become available to researchers. Examples include network data from political campaigns, data from social media generated by individuals, campaign contribution and lobbying expenditure made by firms and individuals, and massive amount of international trade flows data. How can we take advantage of these new data sources and improve our understanding of politics? This course introduces various machine learning methods and their applications in political science research.

Spring 2020, Teaching Assistant

Graduate

Survey of advanced empirical tools for political science and public policy research with a focus on statistical methods for causal inference, i.e. methods designed to address research questions that concern the impact of some potential cause (e.g., an intervention, a change in institutions, economic conditions, or policies) on some outcome (e.g., vote choice, income, election results, levels of violence). Covers a variety of causal inference designs, including experiments, matching, regression, panel methods, difference-in-differences, synthetic control methods, instrumental variable estimation, regression discontinuity designs, quantile regressions, and bounds.