Assisted in designing course modules for factor modeling and risk management. Conducted lab sessions for students using R and Python.

Teaching assistant responsible for grading and tutorial sessions on time-series analysis and high-dimensional data.

This course introduces structured programming and scientific computing using Python and MATLAB, with a strong emphasis on quantitative applications. Core topics include variables, data types, control flow (loops, conditionals), functions, and script organization. Advanced topics cover object-oriented programming, vectorization, numerical linear algebra (matrix operations, eigenvalues, decompositions), optimization routines, and symbolic computation. Students work with libraries such as NumPy, SciPy, pandas, and matplotlib in Python, alongside MATLAB toolboxes for signal processing, optimization, and simulation. Additional topics include file I/O, debugging techniques, performance profiling, parallel computing basics, and GUI development in MATLAB. Applications include solving systems of equations, numerical optimization, time-series simulation, and data visualization. Assignments emphasize implementing algorithms, comparing computational efficiency, and translating mathematical formulations into scalable code.

This course provides a rigorous foundation in statistical theory and applied data analysis using R. Topics include probability theory, discrete and continuous distributions (normal, binomial, Poisson, exponential), sampling distributions, law of large numbers, and central limit theorem. Core inferential methods include point and interval estimation, maximum likelihood estimation, hypothesis testing (t-tests, chi- square tests, ANOVA), and nonparametric methods. Regression topics include simple and multiple linear regression, logistic regression, model diagnostics, multicollinearity, and variable selection techniques. Computational components cover data wrangling (dplyr, tidyr), visualization (ggplot2), simulation methods, bootstrapping, and resampling techniques. Additional topics include time series basics, Bayesian inference introduction, and reproducible research using R Markdown. Students analyze real datasets, perform end-to-end statistical modeling, and interpret outputs critically.

This course focuses on computational techniques used in quantitative finance and financial engineering. Key topics include time value of money, bond pricing, yield curves, duration and convexity, and portfolio theory (mean-variance optimization, CAPM). Derivatives pricing methods include binomial and trinomial trees, Black–Scholes model implementation, Monte Carlo simulation, and variance reduction techniques (antithetic variates, control variates). Stochastic processes such as Brownian motion, geometric Brownian motion, and jump-diffusion models are introduced. Numerical optimization methods are applied to portfolio allocation and risk minimization. Risk management topics include Value-at-Risk (VaR), Conditional VaR, stress testing, and backtesting. Additional coverage includes algorithmic trading basics, high-frequency data handling, and calibration of financial models. Students implement pricing and risk models, analyze financial data, and evaluate model performance under realistic constraints.

This course develops advanced numerical techniques for solving complex financial models. Core topics include root-finding methods (bisection, Newton-Raphson, secant), numerical differentiation and integration (trapezoidal rule, Simpson’s rule, Gaussian quadrature), and interpolation methods (linear, spline, polynomial). Linear algebra techniques include LU, QR, and Cholesky decompositions, and iterative solvers for large systems. Differential equation methods include finite difference methods (explicit, implicit, Crank–Nicolson schemes) for the Black–Scholes PDE, and stability and convergence analysis. Stochastic differential equations are covered through Euler–Maruyama and Milstein schemes. Additional topics include Fourier transform methods for option pricing, sparse matrix techniques, dimensionality reduction, and numerical optimization (gradient descent, quasi-Newton methods). Emphasis is placed on error analysis, computational efficiency, and robustness in implementing financial algorithms.