Teaching

Teaching Experience

Instructional Approach

My pedagogy integrates rigorous economic reasoning with applied computational workflows in quantitative social science. Core instructional design is anchored in Foundations First, Expansion Second, then scaled through the P.R.I.M.E. Prompting Framework. Across courses, students build from theory, measurement, and manual coding toward policy-relevant empirical analysis using Python, Jupyter, and public data infrastructure such as BLS and FRED.

Courses Taught

Applied Data Analytics in Economics (ECON 5200)
Spring 2025 – Present
- Built a Foundations First sequence: manual Python derivations (e.g., OLS/DiD) before GenAI-assisted scaling.
- Designed the P.R.I.M.E. Prompting Framework so students operate as junior analysts with explicit economic verification.
- Integrated experiential labs using Python/Jupyter with real-world BLS and FRED datasets.
Statistical & Machine Learning for Economics (ECON 3916)
Spring 2026
- Designed a concept-extension curriculum linking core econometrics to high-dimensional machine learning.
- Applied Foundations First + P.R.I.M.E. to require manual coding fluency before AI-enabled workflow expansion.
- Covered Double ML, causal forests, Lasso/Ridge, NLP for economists, and predictive modeling with pandas/sklearn/econml.
- Delivered dual-modal instruction via theory seminars and computational labs in Google Colab.
Game Theory (ECON 1916)
Fall 2025
- Prepared a full course sequence on strategic decision-making, Nash equilibrium, and asymmetric information.
- Used simulations and case-based exercises to connect formal models to real-world strategy settings.
Microeconomic Theory (ECON 2316)
2023 – Present
- Taught advanced microeconomics using optimization, equilibrium analysis, and mathematical formalism.
- Integrated policy-relevant topics: public goods, externalities, information asymmetry, and imperfect competition.
- Used quantitative problem-solving and applied projects to bridge theory and empirical practice.
Applied Econometrics (ECON 2560)
Summer 2023
- Delivered training in regression, causal inference, panel methods, and time-series analysis.
- Embedded empirical projects with BLS/FRED data to reinforce applied research skills.
Applied Statistics for Economics (ECON 2560)
Fall 2022
- Taught probability, hypothesis testing, regression, and data visualization for business/economic applications.
- Assigned hands-on projects using Python and Excel for decision-oriented analysis.
Information Economics & Game Theory (ECON 4681)
2020 – Present
- Designed and delivered an applied curriculum on strategic interaction across economics, politics, and business.
- Used in-class games and case studies to teach repeated games, evolutionary dynamics, and asymmetric information.
- Guided students in equilibrium solving and research-oriented topic development.
Principles of Microeconomics (ECON 1116)
2019 – Present
- Delivered active-learning lectures on elasticity, market structure, and externalities with contemporary case applications.
- Implemented mini-projects (e.g., elasticity estimation with CPI/CES data in Excel) and fieldwork-based market analysis.
- Structured collaborative discussion/reporting to strengthen economic reasoning and communication.
Principles of Macroeconomics (ECON 1115)
2017 – Present
- Taught GDP, inflation, unemployment, and cycles using historical and current macro episodes.
- Led group data projects with World Bank indicators (RGDP per capita, inflation, labor participation, savings, investment).
- Integrated post-pandemic policy topics (including IORB) to connect theory with current macro policy debates.
Money and Banking (ECON 3442)
2016 – 2017
- Taught monetary economics, central banking, and financial market structure.
- Connected theory to policy through crisis-era and post-crisis monetary case analyses.
- Facilitated student projects on rates, inflation targeting, and financial stability.