🎓 Four Years at Amherst: A Story of Curiosity, Community, and Conviction

Table of Contents

Writing in progress…

The Beginning

When I arrived at Amherst in the fall, I wasn’t sure what path I would carve out. I only knew two things:

I loved solving problems that felt impossible at first glance.
I wanted to push myself in as many directions as I could.

That restless curiosity soon snowballed into something bigger: a triple major in Computer Science, Mathematics, and Statistics, with each discipline sharpening a different part of my thinking.

Learning to Learn

The most defining part of my Amherst journey wasn’t just the grades (though graduating with a 3.97 GPA and double Summa Cum Laude honors mattered to me), it was learning how to move between abstraction and application.

In Real Analysis and Lie Groups, I wrestled with proofs that demanded patience and precision.
In Advanced Econometrics and Missing Data Methods, I learned how fragile inference can be if assumptions go unchecked.
In Distributed Algorithms, Systems, and Databases, I found joy in building things that scaled beyond toy problems.
And in courses like Craft of Speaking and Philosophy of Progress, I was reminded that numbers mean little unless you can communicate and contextualize them.

Amherst’s liberal arts approach forced me to hold all of these modes of thought together. Looking back, that stretch between rigor and reflection was the best training I could’ve asked for.

Studying Abroad: Columbia University & AIT Budapest Side-Quests

In my freshman summer, I was exploring how I can broaden my horizons and learn more about the world. I came across the full-ride scholarships to study abroad (or away within the US), and that’s how I ended up at Columbia University and AIT Budapest.

Columbia University

…

AIT Budapest

…

Writing Myself Into the Work

By junior year, I wanted to not just consume knowledge but create it. That led me to two theses that became both technical marathons and deeply personal markers of growth:

Mathematics Thesis: Lean4 Machine Assisted Proof Framework for Chip-Firing Games & Graphical Riemann–Roch — my attempt to formalize a piece of combinatorial mathematics in Lean4, bringing computers into the messy beauty of graph theory.
Statistics Thesis: ccrvam: A Python Package for Model-Free Exploratory Analysis of Multivariate Discrete Data with an Ordinal Response Variable — the first time I truly built something for others, an open-source package that future researchers can pick up and extend.

Those projects taught me endurance. Research isn’t about fast answers; it’s about asking the right questions and living with ambiguity until clarity arrives.

Beyond the Classroom

Some of my most vivid memories aren’t tied to lecture halls but to the communities I helped build:

As Founding Director of Engineering at i2i, I led a 20-person team to turn students’ startup ideas into working prototypes.
As President of the Quant Club, I brought together peers to demystify trading and quantitative finance.
Founding the Cricket Club felt personal: bringing a piece of home to Amherst, convincing two math professors (one a former pro cricketer) to become advisors, and seeing students rally around a game they’d never played before.
On the courts, as Head Coach & Lead Shuttler of the Badminton Team, I learned that leadership sometimes looks like booking practice courts, sometimes like giving pep talks before matches, and often just showing up consistently.

These weren’t lines on a resume — they were communities, experiments, and lessons in responsibility.

Mentorship and Paying It Forward

I’ve always believed that knowledge is best when shared. Serving as a Peer Career Advisor at the Loeb Center, I guided fellow students through the maze of tech and quant recruiting.
As a TA and grader across economics, statistics, computer science, and math, I found my voice not just as a student but as a teacher.

I still remember a peer-review comment:

“His ‘office’ in the Science Center became a Schelling point. He would explain concepts over and over until we got it.”

That sentence means more to me than any transcript ever could.

Looking Back

Amherst was never about chasing credentials — though I’ll always be proud of my Phi Beta Kappa induction, Sigma Xi, and Mu Sigma Rho honors.

It was about discovering that curiosity can be a compass, that leadership is about enabling others, and that rigor is meaningless without empathy and communication.

The course lists and theses matter. But more than that, the story is this:

I came in searching for problems to solve.
I left understanding that problems are best solved with people, communities, and conviction.

Looking Forward

As I carry this chapter with me, Amherst feels less like an ending and more like a foundation. The habits of proof, the resilience of research, the joy of teaching, the humility of community — these are what I take with me.

And if you’re reading this as a future Amherst student, wondering if you can stretch yourself too far: you can. And if you lean into it, Amherst has a way of catching you and making the leap worth it.

I am eternally grateful to Amherst College community for the opportunities and experiences I had there. If you’re curious about anything related to my time at Amherst, or starting your own journey, feel free to reach out!

Appendix: Coursework

If you’re curious about the courses I took at Amherst College, here’s a list as far as I can remember:

Amherst College

Quantitative

Advanced Data Analysis
Advanced Econometrics
Real Analysis
Lie Groups & Lie Algebras
Calculus of Variations
Cryptography
Data Science
Complex Analysis
Groups, Rings, and Fields
Intermediate Statistics
Linear Algebra (placed out)
Multivariable Calculus (placed out)
Missing Data Methods
Optimization
Probability (placed out)
Stochastic Processes

Computational

Distributed Algorithms
Data Mining
Algorithms
Databases
Networks
Data Structures
Systems
Theoretical Foundations

Qualitative

Public Choice
Craft of Speaking I
Craft of Speaking II
Writing about Humor
Philosophy of Progress

Columbia University (Graduate Schools of Math and Statistics)

Bayesian Statistics (Graduate)
Statistical Inference (Graduate)
Statistical Methods in Finance (Graduate)
Machine Learning for Finance (Graduate)
Math Methods in Financial Price Analysis (Graduate)
Programming for Quant and Computational Finance (Graduate)

AIT Budapest (Program at Budapest University of Technology and Economics)

Deep Learning (NVIDIA coursework)
Scalable Systems and Development Processes
Mobile Software Development
Applied Cryptography
User Experience Design