Coursework
The following outlines the primary academic focus areas from my graduate and undergraduate training. Both programs were characterized by a strong emphasis on quantitative rigor, computational methods, and the application of theory to real-world problems. For applied work that grew directly from these courses, see the Projects page.
Stevens Institute of Technology — MS Financial Engineering
The MS Financial Engineering program at Stevens Institute of Technology combines rigorous mathematical finance theory with advanced computational methods and direct engagement with real-world market applications. The curriculum is structured around building depth across derivatives pricing, portfolio theory, risk management, and quantitative strategy development, with a consistent emphasis on implementation — requiring students to code, test, and critically evaluate the models they derive. Representative focus areas are listed below (see projects for applied work).
- Portfolio optimization and asset allocation — Mean-variance optimization and the efficient frontier, factor model construction (CAPM and Fama-French), risk-parity and equal-risk-contribution strategies, long/short portfolio construction and target-beta management, and robust estimation techniques to mitigate the sensitivity of optimal weights to input error.
- Derivatives pricing and hedging — Foundational options theory (Black-Scholes-Merton, binomial trees), Monte Carlo simulation for path-dependent and exotic derivatives including Asian, barrier, and lookback options, finite difference methods for PDE-based pricing, and volatility surface modeling including implied volatility, smile fitting, and stochastic volatility frameworks.
- Machine learning for finance — Supervised learning methods including regression, classification trees, random forests, and gradient boosting; unsupervised methods including k-means clustering, Hidden Markov Models, and dimensionality reduction; neural networks and deep learning applied to financial time series, tabular data, and text; and genetic algorithms for combinatorial optimization in portfolio and strategy settings.
- Time series and econometrics — Stationary and non-stationary processes, ARIMA and SARIMA modeling, GARCH and EGARCH for volatility forecasting, cointegration and error-correction models for pairs trading, Kalman filtering for state-space representations, and structural break detection in financial and macroeconomic time series.
- Credit risk and fixed income — Structural and reduced-form credit default models, CDS pricing, collateralized debt obligations and structured credit products, yield curve construction and interpolation, interest rate derivatives pricing under short-rate and HJM frameworks, and duration-convexity risk management for fixed income portfolios.
- Algorithmic trading and quantitative strategies — Systematic strategy design and signal generation, backtesting methodology and common pitfalls (overfitting, look-ahead bias, survivorship bias), transaction cost modeling, market microstructure and order book dynamics, execution algorithms, and performance attribution for quantitative portfolios.
Princeton University — BSE Civil & Environmental Engineering
Minor in Architecture & Engineering. The Civil and Environmental Engineering program at Princeton provided a rigorous foundation in applied mechanics, structural analysis, environmental systems, and quantitative methods, developed through a combination of lecture courses, laboratory work, and studio-based design projects. The minor in Architecture & Engineering extended this foundation into the territory of architectural design and building systems, situating structural and environmental performance within the broader context of spatial and formal design intent. The design studios and engineering analyses that formed the core of this training directly underpin the architecture and research work documented on this site.
- Structural analysis and design — Elastic theory and its application to beams, frames, plates, and shells; matrix structural analysis methods; the design of reinforced concrete structural systems to ACI standards; steel connection design and stability analysis; and load analysis for building systems including gravity, wind, seismic, and thermal loading.
- Environmental systems and sustainable design — Building physics including heat transfer, air and moisture transport, and thermal mass; energy modeling for building performance evaluation; materials science with a focus on construction materials including concrete, steel, and polymer composites; and the design of low-carbon and high-performance building envelopes.
- Architecture and engineering integration — Interdisciplinary design studios at the intersection of structural performance and architectural form, exploring how the technical requirements of spanning, supporting, and enclosing can generate rather than constrain architectural expression.
- Computational and parametric design methods — Form-finding and structural optimization using parametric modeling in Grasshopper and Rhinoceros; Python and IronPython scripting for automation of repetitive design tasks and simulation workflows; and the use of GIS-based tools for large-scale urban and environmental analysis.