// Mathematician · Developer · Researcher
Building rigorous mathematical models at the intersection of finance, statistics, and machine learning. Focused on alpha generation, risk management, and market microstructure.
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I'm a quantitative researcher and developer with deep expertise in statistical arbitrage, derivatives pricing, and systematic trading. My work sits at the convergence of rigorous mathematics and production-grade software engineering.
I believe the best alpha comes from original research — not recycled factor models. My approach emphasizes first-principles thinking, robust backtesting methodology, and careful attention to transaction costs and market impact.
Outside of markets, I contribute to open-source quantitative finance tools and occasionally write about topics like microstructure, regime detection, and the mathematics of risk.
A systematic equity strategy conditioning standard cross-sectional momentum on HMM-identified market regimes, reducing drawdowns 40% vs. naive momentum.
Calibrated a neural stochastic differential equation to SPX option data, achieving superior smile interpolation over classic parametric models like SVI.
Trained a PPO agent to minimize market impact during large order execution, outperforming TWAP and VWAP across various liquidity conditions.
Applied GPD-based EVT to model portfolio tail losses beyond VaR, providing more reliable stress testing under fat-tailed return distributions.
Decomposed term structure movements into orthogonal factors using FPCA, revealing stable level/slope/curvature drivers and predictive macro signals.
A vectorized backtesting library with realistic slippage modeling, event-driven simulation, and portfolio-level risk attribution. 600+ GitHub stars.
Research and deployment of systematic equity and derivatives strategies. Developed signal generation pipeline processing 50TB+ of alternative data. Improved Sharpe ratio of flagship fund by 0.4 through regime-aware position sizing.
Built volatility surface calibration and exotic option pricing models. Reduced model risk P&L by 18% through improved SVI and SABR parameterizations. Implemented real-time Greeks computation system in C++.
Researched intraday liquidity dynamics in the Treasury market using TRACE data. Findings contributed to an internal working paper on market fragility during stress periods.
Thesis: "Optimal Portfolio Construction Under Estimation Risk via Shrinkage Priors." GPA 3.97. Teaching assistant for Stochastic Calculus and Numerical Methods.
Double major with honors. Relevant coursework: real analysis, measure theory, time series analysis, econometrics, numerical linear algebra.
Comprehensive training in portfolio management, derivatives, fixed income, and equity valuation frameworks.
Financial Risk Manager certification covering market risk, credit risk, liquidity risk, and operational risk frameworks.
Interested in discussing research opportunities, quantitative finance problems, or potential collaborations? I'm always open to thoughtful conversations.