About

From mathematics to computer science

I grew up in Kampong Cham and started with mathematics competitions. I was first in my province in Grade 9, national runner-up in Grade 12, and I left school with four full university scholarships. Competition mathematics taught me the habit that this whole site is built on: an answer only counts when you can show that it is right.

I now use the same habit in applied machine learning. I work on Khmer speech recognition because the language I grew up speaking barely exists in the tools I use every day. I work on agritech because the bok choy farmers our team interviewed in Kang Meas plant their fields without knowing who will buy the harvest. In both problems, careful testing matters more than an impressive demo, and the result is useful at home, not only in a paper.

Dual-degree student — Computer Science at Fort Hays State and IT Management at AUPP — and national runner-up in mathematics, 2025. Learning to work as an AI-native developer, and building at CHNAI LAB, a six-member student studio in Phnom Penh, where I work most closely with one teammate.

Why these interests connect

These are not four separate interests. The CS and ITM degrees build the ability to make intelligent systems; the international business studies and field research supply the commercial lens; and the contracts, terms, and policy work asks what those systems are allowed — and ought — to do. Every project on this site touches at least two pillars, on purpose.

In practice that means the same project gets three questions instead of one: does the model work, would anyone pay for it, and what do the contracts, terms, and policies allow it to do? The projects on this site are my attempts to answer all three at once — student-scale work, labeled honestly, receipts attached.

What I can contribute

Shipping working software — TypeScript and Python end to end: a schema-validated Nuxt site whose build fails on unproven claims, a decision engine with 62 unit tests and CI, and a bilingual LMS prototype.
Mathematical grounding — a decade of competition mathematics ending as national runner-up; the habit of proving an answer right before claiming it.
Applied-ML practice with honest evaluation — a public Whisper fine-tuning pipeline for Khmer with published weights, speaker-stratified splits, and self-reported metrics labeled as exactly that.
Field research before code — interviews with real farmers before writing anything, and a decision engine where refusal is a tested, first-class output.
Organizing work around written standards — I lead a small professional reading circle that runs on a handbook I wrote: rotating officer roles, progress measured by what members produce rather than by pages read, and an AI-use policy binding on me as much as on every member.
Bilingual delivery — products, reports, and technical content that work in Khmer and English from the first draft, not as a translation pass.

Skills

Software — TypeScript, Vue/Nuxt, Next.js, Python, unit testing and CI, schema-validated content architectures, and AI-native development: leading coding agents with explicit human review gates.
Machine learning — PyTorch, Hugging Face Transformers, Whisper fine-tuning, CER/WER evaluation design, dataset and manifest discipline; currently studying classic ML fundamentals and C++.
Product & business — structured field research, buyer-first validation, bilingual English/Khmer product design, digital marketing and short-form technical video (CapCut).
Languages — Khmer (native), English (professional working).

How I work with AI

I work AI-natively: AI tools (primarily Claude) support my research, drafting, and implementation, while decisions, evidence labels, and anything that ships stay under human review — mine.

The full policy — what AI assists with and what stays human — lives on the colophon page.