University Teaching

Joe lectures academically on topics in business, politics, economics, and statistics. Subfield topics include political behavior, institutions, research methodology, data science, analytics and NLP. His experience includes the instruction of hundreds of undergraduate and graduate students at top universities and academic conferences, most at Columbia University.

Quality of Instruction. The average student scores Joe’s teaching a 4.8/5 (N=228 reviews submitted, 40% participation rate, 6 courses). His current and previous teaching positions follow.

Topics in Methodology: Introduction to Text-as-Data

Level Upper level course for college juniors, seniors, and graduate students.
Objective Learn how to conduct applied research using text data.
Abstract Topics include a general approach to descriptive and causal inference using text, classical approaches to content analysis, statistical approaches to content analysis, stylometry, the vector space model, feature extraction, dimension reduction, classifiers, topic models, and selected newer topics.

Research Design and Quantitative Methods

Level Two-semester methodology course for college juniors and seniors.
Objective Learn how to conduct applied research using econometric approaches to identification and inference.
Abstract Students learn how to critique research designs by working with a body of scholarly research published in top-tier journals, and then propose a research project of their own. Substantive topics include scientific research design, DAGs, identification and inference, mechanisms, modes of research, and meta-analysis. Quantitative topics include basic probability theory, sampling theory, mechanisms, survey research, ordinary least squares, data visualization, and R.

Topics in Methodology: Scaling of Latent Traits

Level Upper level course for college juniors, seniors, and graduate students.
Objective Understand the theory and practice of scaling, the method that lets us order politicians by ideology, draw inferences from machine analysis of texts, and personalize content at scale with artificial intelligence.
Abstract In this course, we review foundational estimation practices that stretch all the way back Catholic monasteries of the 1400s and into the modern era. Then, we apply the practices of NOMINATE, natural language processing, and joint scaling to study topics in economics, politics, finance, and marketing.

Introduction to American Politics

Level Introductory course, usually required, for college freshmen and sophomores.
Objective Provide an analytical framework with which students may dissect political situations and draw conclusions about the nature of our nation’s people.
Abstract Topics include collective action problems, principal-agent problems, federalism, institutional history, public opinion, political behavior, and elections. We also examine and contest scholarly debates around the causes and consequences of polarization in American politics.

The American Congress

Level Upper level course for college juniors and seniors.
Objective Understand the making of our nation and its laws by it’s most (in)famous political body, Congress.
Abstract In this course, we review the history of the American Congress, its creation under the Constitution, and how its designed solved collective action problems, focusing on works by the founders, Edmund Burke, and Dick Fenno. We then review procedure, bicameralism, supermajoritarianism, committees, and game theory, focusing particularly on work by Greg Wawro, Eric Schikler, and Keith Krehbiel. Finally, we study ideal point estimation, parties, polarization, gridlock, and trends in congressional organization, focusing on work by Matt McCubbins, Steven Smith, and Sarah Binder.

Topics in Politics: Politics and Modern Computing

Level (New!) Upper level course for college juniors, seniors, and graduate students.
Objective Understand the political implications of our nation’s digital transformation, and consider implications for the future.
Abstract New technology has precipitously reduced the costs of voter engagement, district interaction, and labor. This has upset the participatory and representative equilibria which have to this day evolved at a leisurely rate. In this course, we study how a reduction in the costs of collective action by way of digital transformation and artificial intelligence has changed the ways citizens interact with their political institutions.

Student Feedback

Joe was one of the most impressive instructors I have come across to date, across all the professors I've had. Not only is Joe incredibly knowledgeable in the field of politics, but he also possesses the special skill of presenting these concepts in a fascinating, relatable, and clear manner to his students. I highly recommend him to anyone looking for an instructor who is both brilliant and accommodating.

Once in a while, you meet an instructor who is amazing. He taught lectures on statistical programming that were excellent, by the way. Every week he explains concepts in a simple, intuitive way. I am probably as non-mathematically inclined as students come, but, thanks to Joe's patient explanations, I UNDERSTAND STATS. He's super nice and encouraging too, and so wonderfully patient when I ask inanely-phrased questions like "what's this Y change when X changes by one unit thing?" Joe's strong at teaching the Stats portion of things, AND the Theory portion of things.

Provided great insight into the material, and was very helpful in terms of understanding the material better. He was also very approachable in person and by email, and was both efficient and thorough in class.

Joe is a fantastic instructor. He is a very intelligent guy who does a great job at explaining every inch of the material very clearly. He is very easy to approach and always helps you if you have a question. Every class is interesting because he brings in relevant information from our current state as examples, and it really helps us to understand how the system works. His teaching style is very effective and he makes it easy to learn important points in the material.

Teaching Statement

One of the biggest threats to sustained technological progress is the difficulty of integrating instruction in the social, business, and computer sciences. It’s tempting to blame an over-indexed promotion of STEM majors for it…“those arts and sciences students just weren’t made for engineering.” But the difficulty is in my opinion due to a lack of access to cross-pollinated courses of study. What are we doing to demonstrate to computer science students that they can thank Bernard Berelson, a sociologist, for that NLP algorithm they just ran? What are we doing to show arts and sciences students that qualitative inference is an exercise in probability?

The implications of these integration difficulties extend well beyond the ivory tower. We have hundreds of AI-powered companies predicting when you need your laundry picked up and washed, or your food delivered, or your personal finances managed. Yet we atrociously don’t have a widely-used and accessible business intelligence solution in the legal profession. Crossover specialists – people who understand the challenges of their chosen field, but who also understand how a system might be engineered to overcome them – are what we need to mitigate this problem, but they seem to exist primarily in fields where the challenges are commonplace! When we fail to grow crossover specialists in the academy, we fail to provide the talent necessary to overcome the important, domain-specific issues that are endemic to every industry, country, and profession.

Particularly, college graduates of political science and economics – many of whom go onto law school, business school, and other professional degrees – may never get another opportunity to fully understand the power of AI technology for the prediction and analysis of the world around the. This places these graduates at a significant disadvantage, and hobbles the potentially incredible contributions they might have offered.

I aim to overcome this difficulty by introducing new courses into the ordinary social science and business school curricula that introduce these students to such engineering concepts. The courses are anchored and driven by substantive research applications that require deeper consideration of the methods used to develop them. The method is never as important as the actionable insights generated with it. The benefit is that engineering student may cross-register for the courses, and be exposed to an entirely different way of thinking, while entering into collaborative relationships with their peers.