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Dan is a machine learning engineer and lapsed theoretical astrophysicist with a profound love for statistics, research computing, empirical reasoning, and learning. Based in Vancouver, BC, he specializes in transforming recent research into production-ready AI systems.
- A Recipe For AI Strategy (ODSC Featured Article)
- Fooling Fintech AIs (Grow Blog)
- Agile For Research
- Collaborating on Data Science Models
- Statement of Data Science Philosophy
- Guide to running your own web services
- Improving our Teams' Learning Cultures + slides (2024)
- ML and AI for Interstellar Colonization (2021)
- A Recipe for Training Neural Networks (2019)
- Python for High Performance Computing (2013)
Python implementation of the Links online clustering algorithm. Predicts cluster membership without needing to see the entire data set. Featured on paperswithcode.com.
Automated discovery of training problems in PyTorch Lightning models. Implements diagnostic checks to ensure model integrity.
A satirical compiler that bridges any source language to a functional program. A lighthearted take on the complexity of modern development.