Teaching

I teach economics by connecting intuition, formal reasoning, and empirical evidence. My goal is for students to explain an economic mechanism, understand the assumptions behind a model, and use data to reach a defensible conclusion. My experience spans independent instruction in statistics and teaching assistantships across economic theory, econometrics, and data analytics.

Before my Ph.D., I worked in data science and engineering across banking, e-commerce, media measurement, and workforce development. This experience informs my emphasis on careful data construction, reproducible analysis, and clear communication in research and teaching.

Teaching Statement Curriculum Vitae

Teaching Experience

Department of Economics, State University of New York at Binghamton
Fall 2022–Fall 2026

Instructor of Record

Term	Course	Responsibilities
Summer 2025	Statistical Methods	Sole responsibility for the syllabus, learning objectives, lectures, problem sets, data exercises, assessments, and grading.

Teaching Assistant

Term	Course	Responsibilities
Fall 2026 Current	Introduction to Econometrics	Teach econometric foundations and methods; lead R programming, simulation, TA sessions, and office hours.
Spring 2026	Advanced Data Analytics	Supported applied methods and computational work.
Fall 2025	Introduction to Econometrics	Taught econometric foundations and methods; led R programming, simulation, TA sessions, and office hours.
Fall 2024	Corporate Economics	Discussion sections, office hours, and grading.
Spring 2024	Principles of Microeconomics	Discussion sections, office hours, and grading.
Fall 2023	Introduction to Microeconomics	Discussion sections, office hours, and grading.
Spring 2023	Macroeconomics	Discussion sections, office hours, and grading.
Fall 2022	Intermediate Microeconomics	Discussion sections, office hours, and grading.

Teaching Approach

Build intuition before introducing formal methods

I begin with an economic question and the ideas students need to answer it. In microeconomics, a household’s response to a price change provides a starting point for connecting verbal reasoning, diagrams, and optimization. In econometrics, I build from regression foundations to classical assumptions, the Gauss–Markov theorem, hypothesis testing, causal inference, and panel data. I emphasize what each assumption supports and how its failure changes the conclusions students can draw.

Connect statistical reasoning with computation

In R, students connect econometric concepts to data cleaning, estimation, diagnostics, and interpretation. Simulations make abstract problems concrete: generating data with a known parameter and then omitting a relevant variable reveals how an estimate can move away from the truth. Reproducible scripts help students connect each result to the data and decisions that produced it.

Support students as they develop independence

Students bring different levels of preparation in mathematics and programming. I use questions and worked examples to identify whether a difficulty lies in the intuition, algebra, statistical assumptions, or code. In future courses, I plan to pair targeted review and guided practice with increasingly independent assignments, using feedback to help students explain and correct their reasoning.

Courses Prepared to Teach

Core courses: Principles and intermediate microeconomics and macroeconomics; statistics for economists; introductory and intermediate econometrics.

Electives: Applied microeconometrics; causal inference and program evaluation; panel data econometrics; productivity and efficiency analysis; data analytics and machine learning for economists.

Educational Resources in Development

I am developing a learning platform on production functions, empirical industrial organization, and efficiency analysis. It combines foundational explanations with simulations to help students connect economic intuition, formal models, and computational results, and revisit difficult concepts at their own pace.