Teaching

I teach across the quantitative and computational core of the economics curriculum at Pace University, with a focus on empirical methods and applied econometrics. My recurring courses include two Graduate level courses: Data Analysis for R & Python and Applied Econometrics and undergraduate courses: Introduction to Microeconomics, Senior Thesis Course (Seminar in Economic Research), and Economics of AI. Across these courses, my teaching centers on building students’ ability to conduct original empirical research using modern statistical and programming tools.

Teaching Philosophy

I believe the best way to teach economics is through applied real-world examples in an active environment. My pedagogical approach is to challenge students by asking questions and to utilize classroom activities that reiterate the learning objective. This is especially important when teaching econometrics or statistics — the power of these tools is not clearly visible in theory alone, but through application. I believe learning is best applied in a community of active and engaged students. My goal as a teacher is to foster this kind of environment.

Courses at Pace University

Undergraduate

• ECO 106 — Introduction to Microeconomics
• ECO 240 — Quantitative Analysis
• ECO 270 — Economics of the Internet
• ECO 273 — Economics of AI
• ECO 396R — Python & R for Data Analysis
• ECO 400 — Senior Capstone in Economics (Fall)

Graduate

• ECO 585 — Applied Econometrics (Fall) · Course notes on GitHub
• ECO 590 — Python & R for Data Analysis (Spring) · Course notes on GitHub

Previous Teaching

Maastricht Graduate School of Governance

• Introduction to Statistics (TA, Fall 2014)
• Introduction to Data Science (Fall 2015)
• Introduction to Econometrics (TA, Fall 2014, 2015)
• Intermediate Econometrics (TA, Fall 2015)

Maastricht Graduate School of Governance, GPAC (PhD Program)

• Introduction to Panel Data (short course, 2017)
• Introduction to Stata (short course, 2017) · Do-files on GitHub

UNU-MERIT (PhD Program)

• Introduction to Python for Economists (short course, 2017) · Jupyter notebooks on GitHub