I place teaching at the core of my mission as a scholar. Interactions with motivated students and the enlightening discussions that arise in the classroom are responsible for my devotion to education. At Duke University and Humboldt University Berlin, I have taught classes and tutorials on various subjects to diverse groups of students. These experiences have helped me become a better teacher and understand the needs of individual students.
Below is a comprehensive overview of my teaching experience.
Overview of Classes:
Summer 2018: Instructor and TA, Political Science Math Camp, for new graduate students
Summer 2017: Instructor and TA, Political Science Math Camp, for new graduate students
Fall 2016: Instructor and TA, Tutorial and R Lab, Introducing Empirical Approaches to Political Science (graduate class, led by Professor Edmund Malesky) (Syllabus)
Course description: "This course covers basic techniques in quantitative political analysis. It introduces students to widely-‐used procedures for regression analysis, and provides intuitive, applied, and formal foundations for regression and more advanced methods covered in later studies. This course will use rudimentary calculus and matrix algebra rather intensively. This course relies on R for statistical software. This course strives to achieve four overarching goals. First, students will become literate in regression analysis. Even though basic ordinary least squares (OLS) is not still commonly used in many contemporary political science analyses, the regression framework—and related interpretations of marginal effects, hypothesis testing, causal identification, forecasting, and bias-‐efficiency tradeoffs—readily generalizes to more state-‐of-‐the-‐art applications. Second, students will establish a foundation in statistical theory and applied econometrics that will help students move forward with their methods training in the stats, economics and/or political science sequences. Third, students will develop experience working with data on topics related to political science, in the context of in-‐class examples, lab practicums, take-‐home problem sets and a final research paper. Fourth, students will practice applying the quantitative methods to analyses of their own research questions. Students will collect and analyze data as part of a final project."
Summer 2016: Instructor and TA, Political Science Math Camp, for new graduate students
Summer 2016: Instructor, The Political Economy of Competition, Conflict, and Cooperation (Syllabus)
Course description: "Political economy deals with the interactions of states and markets. In other words, it is concerned with the economic effects of political choices and the impact of economic conditions on the decisions and performance of political actors. The goal of this seminar is to provide an overview of how the discipline of political economy theorizes about and empirically investigates competition, conflict, and cooperation. After an introduction to political economy and its methods, several broad questions related to competition are discussed in the first part of the seminar. What are the economic effects of political competition? How do states and firms compete in the global economy for market shares and finances? The seminar then moves on to analyze the political economy of conflict. What are the domestic and international reasons for political conflict and civil war? Under which economic conditions does international conflict take place and how is it economically different from domestic violent conflict? In the third part of the class, we ask the question of how cooperation arises within and between political systems. How does the economic environment shape the emergence of domestic coalitions? Why and how do states cooperate on economic issues? The final topic is the political economy of the modern state and bureaucracy. Which conditions led to the rise of the modern state? How was bureaucratization shaped by industrialization and how does bureaucratic performance affect politics and economic growth?"
Spring 2016: Instructor and TA, Tutorial and R Lab, Maximum Likelihood Estimation (graduate class, led by Professor Christopher Johnston) (Syllabus)
Fall 2015: Instructor and TA, Tutorial and R Lab, Introduction to Empirical Approaches to Political Science (graduate class, led by Professor Kyle Beardsley) (Syllabus)
Course description: "This course covers basic techniques in quantitative political analysis. It introduces students to widely-‐used procedures for regression analysis, and provides intuitive, applied, and formal foundations for regression and more advanced methods covered in later studies. This course will use rudimentary calculus and matrix algebra rather intensively. This course relies on R for statistical software. This course strives to achieve four overarching goals. First, students will become literate in regression analysis. Even though basic ordinary least squares (OLS) is not still commonly used in many contemporary political science analyses, the regression framework—and related interpretations of marginal effects, hypothesis testing, causal identification, forecasting, and bias-‐efficiency tradeoffs—readily generalizes to more state-‐of-‐the-‐art applications. Second, students will establish a foundation in statistical theory and applied econometrics that will help students move forward with their methods training in the stats, economics and/or political science sequences. Third, students will develop experience working with data on topics related to political science, in the context of in-‐class examples, lab practicums, take-‐home problem sets and a final research paper. Fourth, students will practice applying the quantitative methods to analyses of their own research questions. Students will collect and analyze data as part of a final project."
Summer 2015: Instructor and TA, Political Science Math Camp, for new graduate students
Summer 2010: Teaching Assistant, International Relations and World Politics