Finance student with experience in quantitative work, statistical modeling, and system design. Use Excel, R, Python, and low-level C++, and run a Linux homelab for hands-on networking, security, and infrastructure. Focused on risk, prediction, and the link between math, tech, and finance.
Analytical, curious, and motivated to bridge financial intuition with rigorous quantitative tools.
Finance graduate in progress with a background in quantitative analysis and statistical modeling. Build financial models in Excel, R, and Python, while also designing low-level systems in C++ and Linux for secure infrastructure.
Interested in risk, uncertainty, and prediction across poker, sports betting, and financial markets. I combine mathematical reasoning with systems design, which shapes projects like VeloNet and the Axion Communication Protocol.
Hands-on technical support experience, with ownership over data quality and systems reliability.
Finance as a lens on uncertainty, incentives, and long-term decision making.
Tools and skills used to model uncertainty, structure decisions, and communicate results clearly.
Quick visual of how I balance skills I already deploy with areas I am actively deepening.
Low-level C++ and protocol work that connects secure systems, networking, and quantitative thinking.
Selected deep dives that document how I analyze fraud signals and apply ML ideas to finance problems.
Where curiosity compounds into skill: technology, markets, and strategic games.
New to QuantLib but actively formalizing derivative payoffs and term-structure models inside the framework. Each experiment is scoped so it could eventually back a first C++ service that survives production constraints.
Strong practical experience with Linux across multiple distributions plus hands-on work with rack-mounted servers like the Dell R610 homelab that I treat as a private production environment.
Games of chance—poker, exchanges, sportsbooks—treated as quantitative laboratories. I enjoy the mathematics of Bayesian updates, Kelly-style utilities, and stochastic control problems that govern optimal plays under asymmetric information.
Developing predictive engines rooted in stochastic processes inspired by gambling theory. Iteration happens against live datasets to see which signal pipelines keep their edge once uncertainty and noise are introduced.
Best reached via email. GitHub and Carrd show side projects and technical experiments.
Building clarity around messy problems is my favorite collaboration mode. I thrive where feedback loops are tight and stakes are real.
Open to internships, graduate roles, and project-based work in finance, risk, or data-driven decision making.
If you have a problem that lives at the intersection of numbers and uncertainty. I am interested in iterating until the edge appears.