Supported instruction across graduate and advanced undergraduate courses in mathematics, economics, and computational methods. Assisted students with empirical research design, data analysis, and reproducible workflows.
Quantitative Economics
Summer 2023, 2024
Thomas Sargent, John Stachurski & Matt McKay. Covered Python-based computational economics, dynamic programming, and quantitative modeling techniques used in modern macroeconomics research.
Computational & Algorithmic Methods (R)
Fall 2023, Spring 2024
Gabor Nyéki (Princeton) & Zimé Songbian. Algorithmic problem-solving and statistical computing in R, including data wrangling, simulation, and reproducible workflow design for applied social science research.
Computational & Algorithmic Methods (Python)
Fall 2023, Spring 2024
Python-based algorithmic methods covering data structures, numerical computation, and applications to economic modeling, statistical inference, and geospatial analysis.
Econometrics
2024
Tamoghna Hader. Applied econometric theory and practice: OLS, IV, panel data, difference-in-differences, and causal inference methods. Supported lab sessions in Stata and R.
Numerical Analysis
Spring 2023
Guy Degla. Numerical methods for mathematics and economics: root-finding, interpolation, numerical integration and differentiation, and optimization algorithms with applications in economics.
Mathematics for Economics
Fall 2023
Guy Degla. Mathematical foundations for economic analysis: real analysis, topology, optimization, and fixed-point theorems, with applications to consumer and producer theory.
Linear Algebra
Spring 2023
Jonas Doumate. Linear algebra for economics and statistics: vector spaces, linear transformations, matrix decompositions (eigenvalues, SVD), and applications to econometrics and optimization.
Teaching Philosophy
I believe rigorous quantitative training should be grounded in real empirical problems. My approach emphasizes reproducible workflows, intuition-building alongside formal methods, and close mentoring of students through their own research designs — particularly in data-scarce contexts where methodological choices carry significant weight.