Austin Lee

This project takes the information companies generate during software onboarding (comms, including emails, Slack, Jira tickets, and support conversations) and pulls it into one place. The application ingests those signals, organizes them into workstreams, and uses them to generate structured vendor reviews that live on a review board. Over time, it builds a broader layer of onboarding intelligence, with the goal of becoming an end-to-end product for evaluating vendor reliability and surfacing onboarding pain points.

GitHub  ·  Slide Deck

In this project, we use several computer vision techniques to explore which visual features most strongly drive art-style classification. Specifically, we incorporate edge detection, HOG, LAB color features, local binary patterns, and ResNet50 embeddings for this classification task. The model achieved up to 73% accuracy with minimal performance loss after PCA-based dimensionality reduction.

Wildfires in California have become increasingly frequent and severe, posing a significant threat to localities and the state's ecological systems. Using meteorological data (temperature, humidity, wind speed, and other weather patterns) we built models to predict wildfire occurrences. This work has direct real-world implications for local risk assessment, fire department resource planning, and public policy, while surfacing key limitations including temporal scope, spatial resolution, and class imbalance.

Peer-to-peer lending represents a fundamental shift in how individuals access credit, enabling them to bypass traditional financial institutions and borrow directly through online marketplaces. Using Lending Club data, this project investigates what financial factors drive credit pricing in a decentralized marketplace.