The center of gravity is quantitative research — high-performance computing, machine-learning factor models, and quantum computing — with a few applied builds that taught me how to ship. Each card links to a full write-up and, where the repo is public, the code.
A reproducible Fama-French and ML factor-modeling pipeline on fully public data, testing whether sparse factor selections survive a volatility-regime change.
The linear algebra under qubits, gates, entanglement, and measurement, and where quantum amplitude estimation beats classical Monte Carlo for pricing.
Empirical L1-cache characterization of Cholesky, Monte Carlo, GARCH, and GEMM on AMD EPYC, instrumented with PAPI on BigRed200. Every claim traces to a counter. Written up for ACM AIMLSystems 2026 (under review).
An autonomous on-chain trading agent fusing variational-quantum portfolio optimization, Byzantine-fault-tolerant verification, and post-quantum signatures.
An agentic retrieval-augmented pipeline (LangGraph + Qdrant, served on vLLM) for property and M&A document review, built around retrieval faithfulness.
Streams multi-terabyte filesystem inventories in bounded memory to classify storage tiers and emit budget-aware, review-first archival manifests.
