Machine learning, Virginia Tech
I work in machine learning, from optimizers written from scratch to real-time computer vision.
Model design, training, and evaluation
The languages I use day to day
Building and deploying web apps
An interactive site for learning six core ML algorithms. I implemented OLS, gradient descent, SGD, and Adam from scratch, and each training run can be replayed step by step on a live loss chart.
A custom Spotify web player built on the Web Playback SDK. An Express.js backend handles login and API requests, and the app is deployed on Netlify.
Recognizes hand gestures from a webcam in real time. I trained a neural network to 90% validation accuracy across 8 gesture classes, using data augmentation to expand a small training set.
A Kaggle competition on solving olympiad-level math problems with ML. I built three PyTorch models and improved accuracy by 35% with ensembling and hyperparameter tuning.
I collect movement data from Virginia Tech athletes with the Strength and Conditioning program and train models to find connections between weight-room training and competition results.
Organized 20+ competitions a year and coached 30+ youth athletes over five summers.
Bachelor's expected May 2026, accelerated Master's expected May 2027. Focus on machine learning and data-driven systems.