Teaching Philosophy & Courses

Philosophy

My pedagogy is built on the principle of "Concept Extension", a framework where advanced computational techniques are introduced as a scaling of foundational statistical concepts. By grounding machine learning in classical statistics, students develop the intuition of an economist alongside the technical proficiency of a data scientist.

Key Courses

• ECON 5200: Applied Data Analytics in Economics. Bridging economic theory and data science using Python, Jupyter Notebooks, and real-world datasets (BLS, FRED).
• ECON 3916: Statistical & Machine Learning for Economics. Dual-modal instruction combining theory seminars with computational labs in Google Colab (Causal ML, NLP, High-Dimensional Regression).
• ECON 1916 / 4681: Game Theory & Information Economics. Strategic decision-making, Nash Equilibria, and asymmetric information through interactive simulations.
• ECON 1115 / 1116: Principles of Macro/Microeconomics. Modernized to treat "AI as an Economic Force," using experiential learning.
• ECON 2316: Microeconomic Theory. Advanced instruction in utility maximization and market imperfections.