Zoë Farmer

I build production-grade ML systems across a broad range of problem types — classification, forecasting, anomaly detection, and neural network inference. My focus is on models that are accurate, resilient, and fast: efficient data pipelines, careful hyperparameter optimization, and parallel training that holds up under real workloads. I also work across the broader data stack, from interactive visualization to agentic systems.

Selected Projects

Virus Propagation & Markov Chains — Graph-based network infection model exploring total and limited infection algorithms across teacher–student networks, with Markov chain analysis for steady-state propagation. Python

Redistricting via Simulated Annealing — Optimizing political district boundaries using simulated annealing and genetic algorithms. Later adapted into a talk for the Boulder Python Meetup. Python

I also occasionally speak at conferences and meetups — SIAM (2016, 2017), Boulder Python, the Boulder D3 Meetup.