Long before I wrote code, I was creating things in every medium I could get my hands on.
I grew up playing piano, guitar, and bass entirely by ear — listening, imitating,
and building melodies without sheet music. And when I wasn’t making noise,
I was painting or drawing, trying to capture whatever I couldn’t put into words.
Music taught me patterns. Art taught me composition. Both taught me how to see
structure where most people only see chaos.
Eventually that creative instinct spilled into engineering. The same part of me
that used to break down songs or blend colors started breaking down systems —
writing late-night Python experiments, rebuilding websites from scratch, and
teaching myself the math and logic needed to make data behave. It felt less like
switching fields and more like switching instruments.
Today, I’m a Data Science and Computer Engineering intern focused on applied
machine learning, automation pipelines, quantitative modeling, and building systems
that are stable, efficient, and intuitive. I still approach engineering the same way
I approached art and music: start with intuition, refine with structure, and keep
iterating until everything fits.
I’m currently interning at The Johns Hopkins University Applied Physics Laboratory
(JHU/APL) in the Space Exploration Sector, where I help develop mission-critical
software and data infrastructure supporting NASA missions. It’s work that requires
precision, reliability, and a kind of quiet creativity — engineering that feels
more like composition than construction.
In the end, I’m still creating things — only now the canvas is a codebase, the
instruments are models and APIs, and the final product is a system that helps
people solve real problems. The medium changed, but the mindset never did.