I build tools that make sense of data — from statistical simulations to machine-learning pipelines. Below is a selection of projects that showcase what I can do.
I'm a computer science student with a focus on data analysis, statistical modelling, and full-stack web development. I enjoy turning messy datasets into clean insights and building interactive tools that let others explore data intuitively. My work spans Python pipelines, Flask web apps, and front-end interfaces.
A passion project and living resource for higher-level mathematics students. The site aggregates and generates practice questions across college-level topics — from calculus and linear algebra to probability — because well-designed practice problems are the single best study tool I've found.
Currently in active development; new topic modules are added regularly.
This project uses the economy of Team Fight Tactics as a controlled, reproducible probability space. Each "roll" reveals 5 champions from a shared pool; the goal is to acquire 9 copies of any champion (3-star), a champion of a specific cost tier, or a specific unit at a given position. I derived a closed-form expectation for the general case — j successes out of a population of n with k possible successes, replacing only failures — then validated it with Monte Carlo simulation.