I'm Ken Reid — a Senior Data Scientist at Rocket Mortgage with a Ph.D. in Artificial Intelligence from the University of Stirling. My research spans evolutionary computation, optimization, and machine learning, with publications at top venues including GECCO and IEEE SSCI. I design and build data-driven solutions across Python, Java, JavaScript, and more — from automated testing frameworks to large-scale optimization systems.
Peer-reviewed research papers and preprints
This thesis addresses challenging real-world employee rostering and shift scheduling problems using state-of-the-art metaheuristic techniques. Novel approaches including Variable Neighbourhood Search, Evolutionary Ruin & Stochastic Recreate, and hybrid matheuristic methods combining metaheuristics with Integer Programming were developed and evaluated against real-world data provided by BT. The research demonstrates how computational optimization can solve complex, highly-constrained scheduling problems that arise in large-scale workforce management.
Open-source research and data science projects
Factorio game interface for optimizing belt balancer layouts using evolutionary computation. Companion code for the GECCO '21 paper.
Variational Autoencoder for drug discovery — generating novel molecular structures using deep generative models.
Convolutional Neural Network for pneumonia detection from chest X-ray images — a deep learning tutorial for medical imaging.
Machine learning analysis of personal reading data — visualizations, clustering, and predictive modeling on GoodReads export data.
Generalized NLP pipeline — topic modeling, sentiment analysis, and text classification using modern natural language processing techniques.
Machine learning tutorials in Google Colab — hands-on exercises covering core data science concepts and algorithms.
Presented through Michigan State University's BEACON Center for the Study of Evolution in Action. This talk accompanies the paper "The Factory Must Grow: Automation in Factorio" and has been viewed over 21,000 times.
GitHub
Google Scholar
LinkedIn
ORCID iD
ResearchGate