ML + Data Science

ML + Data Science

Work in healthcare, signal processing, machine listening, and ecological audio.

Background

Twenty years of applied machine learning and signal processing across healthcare, music technology, ecological audio, and recommendation systems. Work has included FDA-facing insulin dosing algorithms at Tidepool, large-scale machine listening at Gracenote, machine learning bird detection with the U.S. National Park Service, and recommendation systems at Change.org. Published at ISMIR and ACM SIGIR; multiple patents filed and granted.

Patents and Publications

Healthcare ML Code texture from InsuLearner development

InsuLearner

Open-source algorithms and tools for computing insulin pump settings, with practical integrations for Tidepool and Nightscout data.

Open Project
Ecological Audio U.S. National Park Service acoustic field system illustration

U.S. National Park Service Acoustic Discovery

Machine learning bird-detection tooling for ecological recordings developed with the U.S. National Park Service.

Open Project
Machine Listening Gracenote Mood 2.0 parallel classification architecture diagram

Gracenote Mood 2.0

Archive of my work on deep-learning-based mood classification and music intelligence systems at scale.

Open Project
Learning Tool Precision-recall curve and threshold control illustration

Precision-Recall Visualization Tool

Interactive explainer for thresholding, tradeoffs, and classification thinking.

Open Project

Notes