About

How a curious student became someone building toward research.

Not a résumé — a story. Where the curiosity came from, what bent it toward AI, and the people and habits that keep it honest.

The why
behind the what

Four turns

Why CS

I liked that code was a place where an idea either worked or didn't — no hand-waving. Computer science gave me a way to take a vague question and make it concrete enough to be wrong, which is the first step to being right.

Why Data Science

Statistics is where I learned humility. Adding a data-science focus and a stats minor taught me that the data has a point of view of its own — and that most mistakes happen before any model is trained.

Why Research

Coursework answers questions someone already solved. Research is the first time I got to sit with a question nobody had — and discovered I'd rather live in that discomfort than out of it. It's the only work that consistently keeps me up in a good way.

Why AI

Because the leverage is real, and so is the risk of getting it wrong. I'm drawn to the version of AI that makes a doctor, an operator, or an analyst better at their job — not the version that quietly takes the decision away from them. That distinction is the whole point for me.

The journey

Building a life and a discipline at the same time.

Coming up through the Rutgers Honors College meant learning the work and learning a new place at once. It taught me to be resourceful, to ask for help early, and to treat being an outsider to a field as an advantage — you notice the assumptions everyone else stopped seeing.

What it gave me
Resourcefulness
Figuring it out is a skill, not a fallback.
Perspective
An outsider's eye for unstated assumptions.
Resilience
Comfort with starting before you feel ready.
Mentorship

Teaching is how I check my own understanding.

As an Honors College Peer Mentor, I work with students finding their footing the way I once was. Explaining an idea to someone else is the fastest way to find the holes in your own version of it — mentorship makes me a better researcher, not just a kinder one.

Values
i.Rigor over flash — earn the claim.
ii.Build for the human who bears the consequence.
iii.Curiosity is a discipline you practice daily.
iv.Stay legible — to others, and to yourself.

How I show up

On a team and on my own
Initiative

I don't wait to be handed the problem. I find the gap, scope it, and bring back something working — then ask if it's the right thing.

Teamwork

I'm the teammate who writes the doc, unblocks others, and makes the handoff clean. A result no one else can build on is half a result.

Leadership

As a peer mentor I lead by lowering the barrier for everyone around me — clarity over authority, momentum over noise.

Communication

I can take a dense model and explain it to a clinician, a recruiter, or a teammate — and I write so the next person doesn't have to ask.

Beyond academics

What keeps me level
Cricket & Tennis

Sport taught me that consistency beats intensity, and that you only improve at what you measure honestly — a lesson that travels straight into research.

Fitness

Training is my reset. Showing up on the days I don't feel like it is the same muscle that gets a hard problem across the finish line.

Reading

I collect first sentences. The best papers and the best novels open the same way — they make a promise and then keep it.

Building things

Side projects are where I think with my hands. Half of what I know, I learned by building something slightly beyond me.

Where I'm heading

Toward research that ships — intelligent systems experts actually trust, built in a lab that takes that responsibility seriously.

Building is the goal; the throughline is constant — augment human judgment, stay legible, and earn every claim.

Email me ↗ How I build