Empirical rigor in software and science.

Graduate research in non-stationary stochastic processes laid the groundwork for a career spent building systems that withstand real-world data drift. Across fintech infrastructure and algorithmic trading, the focus has remained constant: code written to be maintained by human beings under high uncertainty.

Whether advising early-stage technical teams or architecting distributed machine learning pipelines, the approach draws on first-principles thinking and rigorous physical note-taking.

A disciplined trajectory across research and industry.

University Research

Fintech Infrastructure

Algorithmic Ventures

Master of Science in Computational Mathematics, focusing on non-stationary stochastic processes and statistical modeling.

Engineering foundational execution engines and reliable data pipelines supporting high-throughput financial markets.

Founding and scaling applied machine learning systems, bridging academic research with commercial product delivery.

Inquiries and collaborations.

Currently engaging with select engineering teams, research groups, and venture partners.