Austin Lee
Analytics Lead · Bluevine
I work on all things data, analytics, strategy, machine learning, and applied AI. I studied economics at UCLA and data science at UC Berkeley, and that intersection of economic thinking and applied ML shapes most of my work. My interests span fintech, experimentation, computer vision, and practical AI applications.
Projects
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.
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.
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Client-approved case study · Fortune 500 outdoor retailer
Designed and analyzed a two-stage randomized experiment to evaluate whether in-store signage increased co-branded credit card tender share across retail departments. Randomized treatment at the store-day level, with activities as within-day controls. Estimated intent-to-treat effects using a linear probability model with store×day and activity fixed effects, weighted least squares to address heteroskedasticity in proportion outcomes, and clustered standard errors. Conducted robustness checks including leave-one-store-out stability analysis and spillover testing across unsigned departments.
Wildfires in California have become increasingly frequent and severe, posing significant threats to communities and ecosystems. Using meteorological data from California's CIMIS weather station network—including temperature, humidity, wind speed, and solar radiation—we built and compared four models to predict wildfire occurrences: logistic regression, random forest, and two feedforward neural network variants. A core challenge was extreme class imbalance, with fire events representing only ~1% of 200k+ observations, which we addressed through class weight balancing and threshold tuning. The random forest classifier emerged as the strongest performer, with wind run ranking as the most predictive feature.
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. We specifically built an OLS regression model to investigate which borrower characteristics and loan attributes drive interest rate pricing. A univariate model on Lending Club's internal credit grade explained over 90% of interest rate variance, revealing grade as a near-black-box pricing mechanism.
Recommendation systems are essential tools that help individuals discover content tailored to their preferences. Whether suggesting movies, music, or products, these systems analyze user interactions and feedback to provide personalized recommendations.
Built an economic analysis comparing traditional property tax and land value tax, with a focus on incentives, efficiency, and market outcomes through game theory. The project modeled how each system affects land improvement, tax burden, and deadweight loss, then applied the framework to a high-level California example.
A terminal-based two-player chess game built entirely from scratch in Python, implementing all six piece types with full legal move validation, check detection, castling, and pawn promotion.
