Projects.

Selected work in data engineering, analytics, design systems, and machine learning — built to solve real problems.

01 project
05 projects
002

Noise-Aware Self-Distillation with Confidence

Noise-aware self-distillation research comparing three approaches for robust image classification on noisy labels. Self-Distill method achieves 54.1% validation accuracy on CIFAR-10N with 40% label noise, outperforming baseline and co-teaching methods.

Python PyTorch Machine Learning Deep Learning
2025
003

Core Entertainment Data Pipeline & Warehouse

Built a pipeline that pulled ticketing, marketing, and financial data out of their silos and into one clean, reliable database. For the first time, Core had a single source of truth for revenue, customer behavior, and campaign performance.

Python SQL Supabase ETL APIs
2024
004

NBA Attendance Analysis: Key Drivers & Revenue Optimization

A data-driven analysis identifying the primary drivers of NBA game attendance and quantifying revenue optimization opportunities. Analyzed 20 years of NBA attendance data (2004–2023) using statistical hypothesis testing and predictive modeling.

Python Pandas Scikit-learn Statistical Analysis
2024
005

NBA Data Analysis

Comprehensive analysis of NBA game data to uncover patterns and insights. Explored game-level statistics, team performance metrics, and player data to identify key trends and relationships across seasons.

Python Pandas Data Analysis Data Visualization
2024
006

Infectious Diseases Data Analysis

Comprehensive data analysis for Infectious Disease Professionals LLC, providing detailed insights on five common infectious diseases: LaCrosse Disease, Lyme Disease, RSV, Tuberculosis, and Hepatitis C. Spans data cleaning to advanced visualizations.

Python SQL Tableau Data Analysis
2024

Technologies & Tools

Python SQL R Tableau Supabase dbt React TypeScript React Native PyTorch Scikit-learn Figma