About

I'm a software engineer and data scientist interested in building systems that work and analyzing problems that matter. Below is how I got here.

Harvey Mudd College

I studied Computer Science at Harvey Mudd College, a school that taught me to think rigorously, to question assumptions, and to measure things carefully. Mudd's emphasis on first-principles reasoning and experimental validation shapes how I approach problems today — whether that's debugging a system or building a statistical model.

Beyond coursework, I worked on systems projects, data pipelines, and analytical models. I learned that the best technical work is paired with clear communication about constraints, trade-offs, and what the evidence actually shows.

Software Engineering

I've built full-stack systems: backend services, frontend applications, databases, APIs, and automation pipelines. I care about shipping real products, designing systems that scale, and writing code that others can understand and maintain.

Early projects included web applications and infrastructure work. Over time, I've developed strong convictions about simplicity, testing, and the importance of knowing your data. A well-designed system is often boring to look at — that's a good sign.

Data Science & Analytics

I discovered I enjoyed the investigative side of engineering: asking "what does the data actually show?" and building models that clarify rather than obscure. This led me deeper into data science and statistics.

I learned to be skeptical of claims without evidence, to validate models carefully, and to communicate results clearly to audiences who don't know the statistical details. A model is only useful if someone acts on it.

Sports Analytics

My love of baseball and basketball intersected with my data skills. Sports are a perfect domain for rigorous analytics: the problems are clear, the data is abundant, and the outcomes are measurable. Pitcher injury prediction, shot value models, player evaluation — these are real puzzles with business impact.

Working in sports analytics taught me to think like a domain expert, not just a statistician. It's not enough to build an accurate model; it has to make sense to scouts, coaches, and front-office staff who will actually use it.

Startups & Building

As I've gained experience, I've become interested in building products and companies. Not just executing on someone else's vision, but identifying real problems and designing end-to-end solutions.

Whether that's a tool I build for myself, a product idea worth exploring, or a startup concept, I'm drawn to the challenge of taking an idea from conception to something people actually use. This requires wearing multiple hats — builder, designer, analyst, communicator.

What Drives Me

I care about evidence over claims. I want to build systems that work. I think deeply about problems before diving into solutions. And I believe the best technical work is paired with clear thinking about why it matters.

I'm most interested in working with people and teams who value rigor, clarity, and shipping real things. If that sounds like something worth exploring, let's talk.