An ongoing project investigating traditional and more sophisticated approaches to statistical arbitrage, specifically pairs trading algorithms.

Tested cross-sectional equity factors on the S&P 100 (2020–2026) with statistical tests, and tested various methods to combine factors into one signal. The highlighted result was a market-neutral signal which was profitable in a bullish and bearish market.

Developing warm-started randomized HOSVD algorithms optimized for GPUs to efficiently conduct lossy compression of streaming tensor data.

Empirical study on VIX-based dynamic hedging during macro shocks. Top project in Erdős Institute Fall 2025 Cohort.

Implemented Monte Carlo pricing for three exotic payoffs (barrier, Asian and lookback) in C++ and CUDA, with a ~10,000× speedup of the GPU pricing kernel over a single-threaded CPU baseline.

Developed automated large-scale data scraping and studied covariance shrinkage techniques for mean–variance portfolio optimization and evaluated their out-of-sample risk–return performance.

Developed a reusable library of numerical analysis and Monte Carlo algorithms with emphasis on stability, convergence, and reproducibility.

Conducted undergraduate thesis research on intersection theory and Schubert calculus, culminating in a defended thesis presentation.

A comprehensive classification of irreducible cubic curves in complex projective 2-space, exploring Bézout's theorem, Cayley-Bacharach theorem, and the algebraic and topological properties of elliptic curves.