About

I started out studying Applied Math at the University of Colorado Boulder, with a Computer Science minor and a tendency to build things in order to understand them. My first research jobs involved applying machine learning to real scientific problems — flagging poor-quality lab photographs at scale, and building massively parallel simulation frameworks for soliton gas dynamics in one-dimensional systems. That research grounding shaped how I approach modeling: start from what the data actually shows, and be honest about uncertainty.

From there I moved into industry — data science consulting at Nidhogg Consulting, public health and global security analytics at Talus Analytics, financial modeling and churn prediction at Red Dot Storage — before landing at Square, where I’ve been since 2021. My work there spans production ML systems across classification, forecasting, and anomaly detection: marketing lead conversion ranking, hardware anomaly detection, web traffic classification. Beyond the models themselves, I care about the ecosystem around them — CI/CD pipelines, code quality, and tooling that lets teams move fast without surprises.

The work that excites me most sits at the intersection of rigor and creativity: finding the right framing for a modeling problem, designing a visualization that makes a complex output actually interpretable, or thinking through how an agentic system should handle uncertainty. I’m drawn to the full stack — from data pipelines and exploratory analysis through deployment and monitoring, and increasingly into LLMs, RAG, and agentic AI.

Outside work, I play Magic: the Gathering, make pottery, draw, read a lot of fantasy novels, and enjoy a good puzzle. I’m based in Toronto, Canada.