I'm going to be honest — when the email arrived on June 15 at 7:30 in the morning, I had just woken up. Still groggy, phone in hand, I almost scrolled past it. The subject line read: "Congratulations! You've been selected for the Stanford AI & Deep Mathematics Research Program." I read it three times before it actually registered.
This blog post is for everyone who asked me "how?", for every student from a small town who ever wondered if the big doors were actually open for them, and honestly, for myself — because writing this down makes it feel real.
Where It All Started
I grew up in Siddharth Nagar, Uttar Pradesh. Not exactly a city people associate with Stanford. My parents aren't researchers or professors — my father runs a small business, and my mother has always been my loudest supporter. Internet was patchy, opportunities were few, but curiosity was something nobody could take away from me.
I got hooked on computers early. By the time I was 16, I was writing scripts to automate things at home just for fun. At 17, I cleared TCS CodeVita and landed a DevOps engineer role at TCS — something I never really shouted about, but it validated something deep inside me: the work was real, and I was capable of it.
"If you're from a place people have never heard of, your job isn't to apologize for it. It's to make them remember it."
Fast forward: Chandigarh University for my B.Tech, the MIT Professional Education Applied Data Science Program, a HackMIT finalist badge, winning the Qualcomm ArkLOL Hackathon 2025 as Lead Developer of Team Invincibles with our AI-powered AR education platform ArkEDU. Each of these built the profile — but more importantly, each of them built the conviction.
Finding the Program
I first came across the Stanford AI & Deep Mathematics Research Program while deep in a research rabbit hole sometime in late March 2026. I was reading about mathematical foundations of neural networks — specifically around geometric deep learning and how Riemannian manifolds show up in transformer architectures. One of the papers I found was co-authored by faculty running this exact program.
The program runs for two months — July 1 through August 30, 2026 — at Stanford University in California. It's intensive: actual research, not coursework. Collaborating with faculty, running experiments, publishing work. The intersection of AI and deep mathematics was precisely what I had been gravitating toward in my own side projects.
I almost didn't apply. The imposter syndrome was loud that week. A 19-year-old from UP, currently juggling university, building a corporate brokerage app, running accessibility-focused AI tools — did I actually belong in that room? I texted Shikha about it. She said, in her characteristically no-nonsense way: "Apply first. Spiral later." So I did.
The Application
The application had three main components: a statement of purpose, a technical portfolio, and two recommendation letters. There was also a short research proposal — one to two pages describing a problem you'd want to work on during the program.
Statement of Purpose
I spent nearly two weeks on mine. The first draft was too formal, trying too hard to sound "Stanford-worthy." My mentor read it and said it didn't sound like me. So I scrapped it and started over — writing about the night I stayed up until 4 AM debugging an edge case in ArkEDU's spatial mapping algorithm, and how that frustration turned into a genuine question about whether our mathematical tools for representing 3D space were even the right ones. That question became the thread running through the entire essay.
Technical Portfolio
I submitted a curated selection of my work: the ArkEDU platform (our Qualcomm Hackathon project), HeroTrack — a full-stack AI productivity web app built with React, TypeScript, Express.js, PostgreSQL, and GPT-4o — and documentation from my CTF and web security research, including my top-3 finish in the openECSC write-up competition. I also included work from my MIT Applied Data Science capstone and an ongoing research note on XSS/XSLeaks attack surfaces.
Research Proposal
My proposal was titled: "Towards Topology-Aware Attention: Applying Persistent Homology to Transformer Self-Attention Patterns." The core idea was that attention heads in large language models seem to learn latent geometric structure that classical linear algebra doesn't fully describe — and that tools from algebraic topology might give us a better lens. Speculative, yes. But it was genuinely mine.
The Interview
About three weeks after submitting, I got a calendar invite for a 45-minute video interview with two faculty members from the program committee. I was terrified. I spent the two weeks before going deep on my proposal, revisiting foundational topology, and reading every paper I could find from the faculty panel.
The interview itself was nothing like I expected. It wasn't a quiz. They weren't trying to catch me out. The first 10 minutes were almost conversational — they asked about my background, how I ended up at Chandigarh University, what drew me to the intersection of AI and mathematics. Then we got into the proposal.
One of the faculty members pushed back hard on a claim I made about persistent homology being "computationally tractable" for large-scale transformers. He was right to push back — I hadn't fully thought through the compute costs at billion-parameter scale. Instead of crumbling, I acknowledged the gap, proposed a possible workaround involving approximation schemes, and said explicitly: "This is exactly why I want to be in a research environment — to stress-test these ideas with people who can see the holes I can't."
That moment, I think, mattered more than any correct answer I gave.
Knowing what you don't know — and being unafraid of it — might be the most important thing you can demonstrate in a research interview.
They also asked about my experience building ArkEDU, specifically the technical trade-offs we made in the AR spatial engine. And they asked a beautiful question at the end: "If you could change one thing about how machine learning is currently taught, what would it be?" I said we teach optimization before intuition — we teach people to minimize loss functions before they understand what loss actually means in the real world. They smiled. That felt good.
The Wait, and the Morning Everything Changed
The wait after the interview was genuinely difficult. Two and a half weeks of checking email compulsively. I kept working — continued building the brokerage app, pushed a new feature on my accessibility AI assistant, went to university, had late-night calls with Shikha about everything except Stanford, because talking about it too much felt like tempting fate.
Received — June 15, 2026 · 7:30 AM