About Me

I studied computer science and physics at Northeastern because I was drawn to both how the world works and how we can build systems to model it. I graduated with honors, but more importantly the combination shaped how I approach problems: start from first principles, understand what is actually happening, and build something practical.

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Wayfair was where I first got a real feel for production engineering at scale. As an intern, I worked on the frontend and backend for internal platform tools and automated CI/CD across enterprise repositories. It was a good introduction to the difference between code that works in isolation and code that other teams rely on every day.

Around that time, I also spent six months at Forschungszentrum Jülich PGI-14 in Aachen as a research intern. I developed C++ and Python libraries for neuromorphic hardware solvers and benchmarked optimization algorithms on a 256-CPU HPC cluster. That work led to a co-authored IEEE ICRC 2024 paper.

From there I joined another startup where I worked on an AI-powered project analysis tool for construction firms. I built a computer vision system to count items across full sets of construction drawings, with the potential to cut manual takeoff time by significant margins. I also helped build the full-stack platform around it with React, Express.js, PostgreSQL, Docker, and Kubernetes.

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I'm most interested in problems where software has to operate in the real world, with messy inputs, edge cases, and real consequences when things go wrong. Agentic systems are a big part of that right now, and I think we're still early in learning how to build them well.

Outside of work, I still follow quantum computing, optimization theory, and computational neuroscience. Those interests are part of the same thread for me: understanding complex systems well enough to make them useful.

You can find more of my work on GitHub, or reach out on LinkedIn.