ML Systems Engineer
Building intelligent systems
for underground mining.
I build machine learning systems for underground mining. My PhD research focuses on predicting energy and fuel consumption for mining vehicles using everything from physics-based models to foundation models — testing which approaches actually scale. I apply the Bitter Lesson to an industry that desperately needs it: simple, data-driven methods that get better with more data beat brittle, hand-crafted systems every time.
Projects
View all →BEV Energy Consumption Prediction
Comparative evaluation of physics-based models vs. ML (XGBoost, LSTM, TCN) for predicting energy consumption of a Sandvik LH518B battery-electric underground loader. 172k data points at 10Hz from real mine telemetry.
Mining Duty Cycle Classification
Self-supervised learning for classifying mining vehicle operational states (loading, hauling, tramming, idling) from raw telemetry — eliminating the need for manual labeling across vehicle fleets.
Diesel Fuel Consumption Prediction
Physics vs. ML comparison for diesel fuel prediction on a Caterpillar R2900G underground loader. Collaboration with CanmetMINING (Natural Resources Canada). AI models achieved 91% error reduction over physics baselines.
AI Scheduling App
Natural language-powered scheduling system for auto mechanic shops. Locally-run fine-tuned LLM parses scheduling requests into constraints, solved by a custom constraint satisfaction engine.