I am a software engineer and quantitative trader working at the intersection of production systems architecture and statistical modeling. My background spans building low-latency algorithmic trading infrastructure, engineering mathematical models, and researching the mechanistic interpretability of transformer networks. I hold a B.S. in Computer Science and Mathematics from Duke University, where I graduated as an Angier B. Duke Scholar.
Designed and deployed algorithmic trading software and quantitative research strategies within fixed income and bond markets. Devised and trained core signal models using Python, and engineered robust, production-critical execution systems natively within the firm's OCaml infrastructure.
Conducted research focused on the mechanistic interpretability of transformer-based language models. Designed and implemented causal scrubbing frameworks to isolate and analyze in-context linear regression processes inside transformer layers.
Quantitative Trading Intern (Jane Street, Summer 2022): Formulated statistical pricing architectures for security baskets, implementing heteroscedasticity prediction components to optimize legacy autoregressive engines.
Research Assistant (Laber Labs, Spring 2022 — May 2023): Built algorithmic policies for multi-armed bandit strategies to evaluate policy optimization metrics in mobile healthcare initiatives.
Graduated Magna Cum Laude with a 3.97 GPA. Recipient of the Angier B. Duke Scholar flagship merit award.