Graduate coursework underpinning the quantitative, computational, and machine-learning methods behind this work.
← Back to portfolioA rigorous treatment of the probabilistic foundations underlying continuous-time finance, including Brownian motion, martingale theory, and stochastic differential equations. The course develops the Itô calculus machinery used to model asset price dynamics and builds toward the mathematical underpinnings of derivative pricing and risk-neutral valuation.
A study of the term structure of interest rates and the instruments built on it, from zero-coupon bonds through mortgage-backed securities and interest rate derivatives. The course covers duration, convexity, and key term-structure models used to price and hedge interest-rate-sensitive portfolios under shifting rate environments.
An examination of option and forward pricing theory, including the Black-Scholes-Merton framework, binomial lattice methods, and the Greeks used to manage derivative exposure. The course emphasizes the no-arbitrage principles and hedging strategies that connect theoretical pricing models to practical trading and risk management.
A foundational course in modern portfolio theory, asset pricing, and market efficiency, covering mean-variance optimization, the Capital Asset Pricing Model, and multi-factor extensions. The course connects theoretical asset pricing frameworks to real-world portfolio construction and performance evaluation.
A computational systems course focused on architecting and building robust, scalable software for data-intensive applications. The course covers system design principles, modular architecture, and implementation practices relevant to building production-grade financial and quantitative systems.
An applied course in machine learning and natural language processing methods for financial text and market data, spanning dictionary-based and embedding-based sentiment analysis, transformer fine-tuning, retrieval-augmented generation, and large language model inference. This coursework directly underlies the six projects featured in the Research & Projects section of this site.
A course in the numerical techniques used to solve the partial differential equations and stochastic models that arise in quantitative finance, including finite difference schemes, Monte Carlo simulation, and numerical linear algebra. The course bridges continuous-time theoretical models with the computational methods required to implement them in practice.