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, Humboldt University Berlin, the University of Virginia, and the University of Konstanz, I have taught classes and tutorials on various subjects to diverse groups of students. These experiences have helped me become a better teacher and more fully understand the needs of individual students.

Below is a comprehensive overview of my teaching experience.

Overview of Classes:

Fall 2024: Instructor, Advanced Quantitative Methods in Political Science: Causal Inference, Panel Data, and Multilevel Modeling (Draft Syllabus Download)

Course Description: This course provides students with a comprehensive overview of advanced quantitative applications in political science, including methods of causal inference, panel data analysis, and multilevel modeling. The key goal is to equip the participants with both a strong theoretical background in these methods and the knowledge to apply them in future research projects. The course builds upon skills acquired in foundational methods courses (Methods I and Methods II at Aarhus University) and does not presuppose knowledge of mathematics or probability theory beyond what is introduced there. It consists of four different parts: (1) mathematical foundations, (2) theory and application of causal inference, (3) theory and application of panel data analysis, and (4) theory and application of multilevel modeling. In the first part of the course, because a thorough understanding of advanced quantitative methods requires a strong mathematical foundation, we begin with a review of the concepts in probability theory and regression analysis that are most directly relevant to us. In the second part of the course we discuss the mathematical theory and application of key causal inference tools, including matching, regression discontinuity designs, instrumental variables, and differences-in-differences. In the third part of the class, we consider different types of panel data analysis, including fixed effects models, random effects models, panel models with instrumental variables, and dynamic panel models. In the fourth and final part of the course, we introduce multilevel modeling, including multilevel linear regression, multilevel logistic regression, and multilevel generalized models. During class, students will participate in a series of replication exercises through which they will learn how to apply these tools themselves. By the end of the course, students will formulate their own research question and present a research design that makes use of one of one of the advanced methods they have learned about.

Winter 2023–2024: Instructor, Mathematics for Political Science (Syllabus)

Course description: The application of rigorous statistical methods is a core aspect of modern political research. Moreover, many key contributions to political science and political economy are based on game theoretic modeling. In order to fully understand these statistical and game theoretic approaches, comprehensive knowledge of the underlying mathematical tools is essential. Therefore, this class introduces students to a number of topics in mathematics that are a prerequisite to advanced classes in methodology: (1) We begin with a quick introduction to the fundamentals of mathematics, including mathematical notation, functions, limits, and other basic topics. (2) Then we study calculus in one dimension, including differentiation, integration, and the identification of extrema. (3) Probability theory is an essential building block of mathematical statistics, which is the reason for us to devote a significant amount of time to this topic. (4) The fourth topic is linear algebra, including systems of equations, Eigenvalues, and Markov chains. (5) Finally, the class closes with an introduction to multivariate calculus. Knowledge of all of these tools will enable the students to subsequently take more advanced methodological classes in statistics and game theory.

Summer 2023: Instructor, Causal Inference in Political Science Research (Ph.D.-Level Class) (Syllabus)

Course description: A large share of advanced research in political science theorizes about and tests causal relationships between social, economic, and political phenomena. Given the importance of identifying causal relationships to state-of-the-art research in the discipline, this class aims to introduce graduate students to the mathematical foundations, theory, and methods of causal inference. After an initial review of probability theory and regression analysis, students learn about directed acyclic graphs and the potential outcomes model, which will be the theoretical basis for the remainder of the class. In subsequent sessions, the class will cover the key methods of causal inference with observational data. Specifically, students will learn about the following topics: (1) matching, (2) regression discontinuity, (3) instrumental variables, (4) panel data, (5) differences-in-differences, and (6) synthetic controls. An essential component of the class will be student-led presentations and replication exercises of outstanding recent research articles in political science. These exercises are meant to help students bridge the gap between the theory and practice of causal inference.

Summer 2023: Instructor, Mathematics for Political Science (Syllabus)

Course description: The application of rigorous statistical methods is a core aspect of modern political research. Moreover, many key contributions to political science and political economy are based on game theoretic modeling. In order to fully understand these statistical and game theoretic approaches, comprehensive knowledge of the underlying mathematical tools is essential. Therefore, this class introduces students to a number of topics in mathematics that are a prerequisite to advanced classes in methodology: (1) We begin with a quick introduction to the fundamentals of mathematics, including mathematical notation, functions, limits, and other basic topics. (2) Then we study calculus in one dimension, including differentiation, integration, and the identification of extrema. (3) Probability theory is an essential building block of mathematical statistics, which is the reason for us to devote a significant amount of time to this topic. (4) The fourth topic is linear algebra, including systems of equations, Eigenvalues, and Markov chains. (5) Finally, the class closes with an introduction to multivariate calculus. Knowledge of all of these tools will enable the students to subsequently take more advanced methodological classes in statistics and game theory.

Winter 2022–2023: Instructor, The Historical Political Economy of Bureaucracy (Syllabus)

Course description: Modern bureaucratic systems are powerful tools that allow political leaders to implement policies, regulate economies, and manage social interaction. Given the capabilities of these administrative organizations, they can be used to promote sustained and inclusive economic growth as well as human development. Yet bureaucracies can also serve authoritarian purposes, which may include economically benefiting a small social group and suppressing political opposition. Finally, bureaucracies and bureaucrats can become politically influential actors themselves. Thus, the importance of modern bureaucracies for determining the fates of societies is difficult to overestimate. Given both the political and economic relevance of modern bureaucracies, this course focuses on the historical political economy of bureaucracy. The class is organized as follows: (1) It begins with an overview of what “modern bureaucracy” is and why we study it. Subsequently, we seek to answer a number of crucial questions: (2) How can we classify different bureaucratic systems and what are the key distinctions between them? (3) What were the broader causes of the historical emergence of modern bureaucracies (bureaucratization)? (4) Which factors explain variations the design and performance of these early administrative systems? (5) How did bureaucracies historically affect their socioeconomic and political environments? (6) What are global and international dynamics of long-term bureaucratic development? (7) Finally, can we apply what we have learned about bureaucratic history to the analysis of present-day administrative systems? Throughout the class, we will highlight the role that bureaucracies play in state-market interactions—the key subject of historical political economy.

Winter 2022-2023: Instructor, Mathematics for Political Science (Syllabus)

Course description: The application of rigorous statistical methods is a core aspect of modern political research. Moreover, many key contributions to political science and political economy are based on game theoretic modeling. In order to fully understand these statistical and game theoretic approaches, comprehensive knowledge of the underlying mathematical tools is essential. Therefore, this class introduces students to a number of topics in mathematics that are a prerequisite to advanced classes in methodology: (1) We begin with a quick introduction to the fundamentals of mathematics, including mathematical notation, functions, limits, and other basic topics. (2) Then we study calculus in one dimension, including differentiation, integration, and the identification of extrema. (3) Probability theory is an essential building block of mathematical statistics, which is the reason for us to devote a significant amount of time to this topic. (4) The fourth topic is linear algebra, including systems of equations, Eigenvalues, and Markov chains. (5) Finally, the class closes with an introduction to multivariate calculus. Knowledge of all of these tools will enable the students to subsequently take more advanced methodological classes in statistics and game theory.

Summer 2022: Instructor, Causal Inference in Political Science Research (Ph.D.-Level Class) (Syllabus)

Course description: A large share of advanced research in political science theorizes about and tests causal relationships between social, economic, and political phenomena. Given the importance of identifying causal relationships to state-of-the-art research in the discipline, this class aims to introduce graduate students to the mathematical foundations, theory, and methods of causal inference. After an initial review of probability theory and regression analysis, students learn about directed acyclic graphs and the potential outcomes model, which will be the theoretical basis for the remainder of the class. In subsequent sessions, the class will cover the key methods of causal inference with observational data. Specifically, students will learn about the following topics: (1) matching, (2) regression discontinuity, (3) instrumental variables, (4) panel data, (5) differences-in-differences, and (6) synthetic controls. An essential component of the class will be student-led presentations and replication exercises of outstanding recent research articles in political science. These exercises are meant to help students bridge the gap between the theory and practice of causal inference.

Summer 2022: Instructor, Mathematics for Political Science (Syllabus)

Course description: The application of rigorous statistical methods is a core aspect of modern political research. Moreover, many key contributions to political science and political economy are based on game theoretic modeling. In order to fully understand these statistical and game theoretic approaches, comprehensive knowledge of the underlying mathematical tools is essential. Therefore, this class introduces students to a number of topics in mathematics that are a prerequisite to advanced classes in methodology: (1) We begin with a quick introduction to the fundamentals of mathematics, including mathematical notation, functions, limits, and other basic topics. (2) Then we study calculus in one dimension, including differentiation, integration, and the identification of extrema. (3) Probability theory is an essential building block of mathematical statistics, which is the reason for us to devote a significant amount of time to this topic. (4) The fourth topic is linear algebra, including systems of equations, Eigenvalues, and Markov chains. (5) Finally, the class closes with an introduction to multivariate calculus. Knowledge of all of these tools will enable the students to subsequently take more advanced methodological classes in statistics and game theory.

Winter 2021–2022: Instructor, The Historical Political Economy of Bureaucracy (Syllabus)

Course description: Modern bureaucratic systems are powerful tools that allow political leaders to implement policies, regulate economies, and administer social interaction. Given the capabilities of these administrative organizations, they can be used to promote sustained and inclusive economic growth as well as human development. Yet bureaucracies can also serve authoritarian purposes, which may include economically benefiting a small social group and suppressing political opposition. Finally, bureaucracies and bureaucrats can become politically influential actors themselves. Thus, the importance of modern bureaucracies for determining the fates of societies is difficult to overestimate. Given both the political and economic relevance of modern bureaucracies, this course focuses on the historical political economy of bureaucracy. The class is organized as follows: (1) It begins with an overview of what “modern bureaucracy” is and why we study it. Subsequently, we seek to answer a number of crucial questions: (2) How can we classify different bureaucratic systems and what are the key distinctions between them? (3) What were the broader causes of the historical emergence of modern bureaucracies? (4) Which factors explain variations the design and performance of these early administrative systems? (5) How did bureaucracies historically affect their socioeconomic and political environments? (6) What are global and international dynamics of long-term bureaucratic development? (7) Finally, can we apply what we have learned about bureaucratic history to the analysis of present-day administrative systems? Throughout the class, we will highlight the role that bureaucracies play in state-market interactions—the key subject of historical political economy.

Winter 2021–2022: Instructor, Mathematics for Political Science (Syllabus)

Course description: The application of rigorous statistical methods is a core aspect of modern political research. Moreover, many key contributions to political science and political economy are based on game theoretic modeling. In order to fully understand these statistical and game theoretic approaches, comprehensive knowledge of the underlying mathematical tools is essential. Therefore, this class introduces students to a number of topics in mathematics that are a prerequisite to advanced classes in methodology: (1) We begin with a quick introduction to the fundamentals of mathematics, including mathematical notation, functions, limits, and other basic topics. (2) Then we study calculus in one dimension, including differentiation, integration, and the identification of extrema. (3) Probability theory is an essential building block of mathematical statistics, which is the reason for us to devote a significant amount of time to this topic. (4) The fourth topic is linear algebra, including systems of equations, Eigenvalues, and Markov chains. (5) Finally, the class closes with an introduction to multivariate calculus. Knowledge of all of these tools will enable the students to subsequently take more advanced methodological classes in statistics and game theory.

Spring 2021: Instructor, The Political Economy of the Modern State and Interstate System (Syllabus)

Course description:The modern state represents one of the most fascinating organizational achievements in human history. After it emerged in the late medieval and early modern periods, not only did it persist for centuries but it also shaped the course of civilization. Regardless of whether or not citizens of modern states are aware of it, the state’s organization and the quality of its institutions—especially the performance of modern public bureaucracies—have wide-ranging, fundamental, and multifaceted impacts on social structures, economic growth, and human development. Therefore, understanding the modern state’s organization is essential to understanding political economy: a field focused on the interactions of governments and markets. Given the modern state’s relevance, this course seeks to familiarize students with its key characteristics, its historical development, its impact, and the challenges it awaits. We ask: What distinguishes the modern state from other types of (political-administrative) organization? Which social, economic, and technological circumstances facilitated its emergence? In turn, how do states influence their socioeconomic environments? Furthermore, how do states interact with each other on the international stage? And what does the future hold for the modern state? Specifically, will it survive global economic integration? The class is split into four parts that are described in more detail in the course schedule.

Fall 2019: Instructor, Global Political Economy (Syllabus)

Course description:The organization of the global political economy has significant effects on our daily lives. Barriers to the exchange of goods can raise the prices of consumer products, financial crises can lead to the collapse of entire economies, and the ability of nations to forge mutually beneficial trade relationships might prevent the occurrence of major war. Despite the global political economy’s relevance, few people are familiar with its actors, institutions, and historical development. This course seeks to familiarize students with international political-economic institutions and processes. Moreover, an important prerequisite for comprehending how states and markets interact at the global level is a thorough understanding and knowledge of their domestic organization. Therefore, this seminar aims at introducing students to the fundamental theories, facts, and historical knowledge of both domestic and global political economic organization. The class is split into four parts that are described in more detail in the course schedule.

Summer 2018:TA and Instructor, Political Science Math Camp & Introduction to R and LaTeX, for new graduate students

Course description: The political science math camp is based on the book Mathematics for Political and Social Research (Moore & Siegel). It covers the following topics: (i) basics of mathematics, (ii) calculus, (iii) probability, (iv) linear algebra, and (v) multivariate calculus and optimization. The course is intended to prepare students for later methods classes, provide students with a basic understanding of a variety of mathematical skills, and explore the importance of these skills in the context of social science research. The orientation following the math camp also introduces students to the statistical programming language R and the document preparation system LaTeX. (Abstract partially based on the course description.)

Summer 2017: TA and Instructor, Political Science Math Camp & Introduction to R and LaTeX, for new graduate students

Course description: The political science math camp is based on the book Mathematics for Political and Social Research (Moore & Siegel). It covers the following topics: (i) basics of mathematics, (ii) calculus, (iii) probability, (iv) linear algebra, and (v) multivariate calculus and optimization. The course is intended to prepare students for later methods classes, provide students with a basic understanding of a variety of mathematical skills, and explore the importance of these skills in the context of social science research. The orientation following the math camp also introduces students to the statistical programming language R and the document preparation system LaTeX. (Abstract partially based on the course description.)

Fall 2016: TA and Instructor of the 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: TA and Instructor, Political Science Math Camp & Introduction to R, for new graduate students

Course description: The political science math camp is based on the book Mathematics for Political and Social Research (Moore & Siegel). It covers the following topics: (i) basics of mathematics, (ii) calculus, (iii) probability, (iv) linear algebra, and (v) multivariate calculus and optimization. The course is intended to prepare students for later methods classes, provide students with a basic understanding of a variety of mathematical skills, and explore the importance of these skills in the context of social science research. The orientation following the math camp also introduces students to the statistical programming language R . (Abstract partially based on the course description.)

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: TA and Instructor of the Tutorial and R Lab, Maximum Likelihood Estimation (graduate class, led by Professor Christopher Johnston) (Syllabus)

Course description:This class covered a wide range of topics related to maximum likelihood estimation. Topics included (1) probit and logit models for binary dependent variables, (2) ordered probit and ordered logit models for ordinal-scale dependent variables, (3) multinomial logit models for nominal-scale dependent variables, (4) poisson, quasi-poisson, and negative binomial model models for count dependent variables, (5) tobit models for truncated and censored variables, and (6) an introduction to multilevel modeling.

Fall 2015: TA and Instructor of the 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: TA and Instructor, Political Science Math Camp & Math Camp Troubleshooting, for new graduate students

Course description:The political science math camp is based on the book Mathematics for Political and Social Research (Moore & Siegel). It covers the following topics: (i) basics of mathematics, (ii) calculus, (iii) probability, (iv) linear algebra, and (v) multivariate calculus and optimization. The course is intended to prepare students for later methods classes, provide students with a basic understanding of a variety of mathematical skills, and explore the importance of these skills in the context of social science research. (Abstract partially based on the course description.)

Summer 2010: Teaching Assistant, International Relations and World Politics

Course description:The class introduced the students to international relations and world politics. After an overview of the history of the discipline, the most important strands of international relations theory were introduced, including realism, liberalism, and constructivism. An essential part of the class were working groups which focused on specific countries or world regions to apply theories to specific settings and test their explanatory power.