VR Attention Detection & Productivity System
I'm deeply interested in VR as a medium and wanted to study it rigorously. This project came from wanting to understand not just how to build in VR but how the environment itself affects the person inside it.
A real time system that uses gaze tracking and behavioral signals from a Meta Quest headset to model attention states during VR work sessions. Detects focus drops and environmental distractors, logging session data for analysis and surfacing personalized productivity insights.
Python
Unity
C#
Meta Quest / OpenXR
PyTorch
ERCOT Net-Load Forecasting Model
AI is reshaping every major industry, and energy is one of the most obvious and important. I was curious about what applied ML actually looks like in a domain with real infrastructure stakes, so I picked a classic forecasting problem and built a real solution.
A gradient-boosted forecasting pipeline for net load prediction on the ERCOT grid, incorporating weather features, calendar effects, and historical demand. Trained on multiple years of public ERCOT data with cross-validated evaluation and an automated retraining workflow.
Python
XGBoost
FastAPI
Docker
GCP
Distributed Job Queue
I wanted genuine hands-on experience with foundational systems that underpin modern AI infrastructure. I identified a classic problem in distributed computing, built a clean solution, and learned more from that than from any tutorial.
A distributed task queue built from scratch with worker processes, a coordinator node, and a persistent broker. Supports job prioritization, retries, dead letter handling, and observability hooks. Designed as a learning project for understanding the primitives beneath tools like Celery and RQ.
Python
FastAPI
Docker
Redis
Git