I work where business and technical systems meet.
I’m interested in the operating layer between a good idea and a reliable result: requirements, data, process, incentives, models, testing, communication, and follow-through.
Where I’m coming from
I graduated from the University of Washington Bothell in August 2026 with a B.A. in Business Administration, a 3.64 GPA, and an academic focus in Finance and Management Information Systems. My strongest work has rarely stayed inside a single discipline.
In nonprofit operations, I was managing people and recurring reporting while also building Excel controls and handling grant budgeting. In academic finance, I built valuation and capital-budgeting models but spent just as much time reconciling assumptions and interpreting why different methods disagreed. In System, my personal software project, I own product behavior, analytics rules, sync/history semantics, validation, and AI-assisted development workflows.
How I use AI
I use AI extensively, but I try to separate leverage from judgment. AI helps me implement, debug, research, compare alternatives, expand test coverage, and coordinate multi-step work. I keep the requirements, evidence boundaries, validation criteria, and final interpretation explicit.
That distinction became important enough that I built a GitHub-backed relay system for multi-worker AI coordination. The automation handles task state and handoffs; I still decide what the product should do and whether the result is actually correct.
How I handle uncertainty
When I can get the missing information, I would rather say “I don’t know yet” and research it than manufacture certainty. When a decision cannot wait, I work with ranges and scenarios: identify the variables that could move, test the downside, and look for a solution that still works across several plausible outcomes rather than one fragile point estimate.
That mindset shows up in very different work—from Monte Carlo modeling to product rules and operating decisions. The goal is not to predict every edge case. It is to make the decision robust enough that an unexpected case does not make the whole system fail.
My working loop
Understand who is making the decision, what information they have, where the workflow breaks, and which constraints actually matter.
Create the smallest useful model, workflow, prototype, requirement set, or decision framework that makes the next step clearer.
Use tests, reconciliation, sensitivity, source checks, and field feedback instead of trusting the first output because it runs.
Make progress, assumptions, and ownership visible early enough that other people can challenge the work before errors become expensive.
Education
Academic focus in Finance and Management Information Systems. Annual Dean’s List, 2024–2025.
Credentials
Capabilities
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