Parker Hayashi

Machine learning pipeline that predicts professional ATP tennis match outcomes from decades of historical data.

• Developed a machine learning model to predict ATP tennis match outcomes using 50+ years of match data
• Engineered 100+ features including Elo ratings, surface stats, and head-to-head history
• Achieved high prediction accuracy via XGBoost with cross-validation and isotonic calibration

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Full-stack dashboard for macroeconomic indicators with data from the Federal Reserve Economic Data (FRED) API.

• Developed a full-stack economic dashboard with React for interactive visualization of 500+ economic time series
• Engineered a scalable FastAPI backend to retrieve and serve data from the FRED API
• Implemented client-side filtering, multi-series comparisons, and adaptive layouts using React hooks, conditional rendering, and Flexbox/Grid

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Multimodal machine learning framework for overall survival prediction in non-small cell lung cancer (NSCLC).

• Developed a multimodal XGBoost-based model to predict NSCLC overall survival using 36 NLP-processed clinical and genomic features
• Achieved up to 0.84 AUROC, outperforming previous state-of-the-art NSCLC survival models by up to 16.67%
• Provided a tool to help physicians personalize treatment strategies and improve patient quality of life
• Awarded 1st Place Overall and Human Health Category at the 2024 International Forum on Research Excellence

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Real-time compost bin monitoring platform for live capacity insights across a large-scale composting network. With Dan Li, Deep Patel, and Ryan Shin.

• End-to-end monitoring workflow with live operational visibility for distributed bin networks

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