About

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. I'm currently pursuing a master's degree in Organizational Leadership, pairing the technical side of my work with the people side of leading teams.

Research Interests

Data Science Optimization Evolutionary Computation Generative AI Machine Learning Genetic Programming Metaheuristics Deep Learning

Keywords

Python Java SQL R Lua Pandas NumPy Scikit-learn TensorFlow Jupyter LEAP & DEAP Hyperparameter Tuning Docker & Kubernetes Git Linux
MH Veiga, KN Reid, B Modenesi, J Boyko, N Fox, T Richter, D Kwon, A Omid, et al.
Google Scholar listing — 2026
Y Song, AR Bevington, KN Reid, J Zhu, Y Liu, K Zhu
The Journal of Open Source Software 11(119):9268 — 2026
54 citations
J Boyko, J Cohen, N Fox, MH Veiga, JI Li, J Liu, B Modenesi, AH Rauch, et al.
arXiv preprint arXiv:2311.04929 — 2023
7 citations
SS Li, H Peeler, AN Sloss, KN Reid, W Banzhaf
Proceedings of the Genetic and Evolutionary Computation Conference Companion — 2022
14 citations
H Peeler, SS Li, AN Sloss, KN Reid, Y Yuan, W Banzhaf
Google Scholar listing — 2022
Show 5 earlier papers (2016–2021)
9 citations
KN Reid, I Miralavy, S Kelly, W Banzhaf, C Gondro
GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference — 2021
58 citations
J Han, C Gondro, K Reid, JP Steibel
G3 11(7), jkab032 — 2021
13 citations
KN Reid, J Li, A Brownlee, M Kern, N Veerapen, J Swan, G Owusu
Proceedings of the Genetic and Evolutionary Computation Conference, 1311-1318 — 2019
2 citations
KN Reid, J Li, N Veerapen, J Swan, A McCormick, M Kern, G Owusu
2018 10th Computer Science and Electronic Engineering (CEEC), 19-23 — 2018
9 citations
KN Reid, J Li, J Swan, A McCormick, G Owusu
IEEE SSCI 2016 — 2016

Doctoral Thesis

Metaheuristics for Solving Real World Employee Rostering and Shift Scheduling Problems
University of Stirling · July 2019 · Supervised by Dr Jingpeng Li · Funded by BT and the EPSRC DAASE Project

Real-world employee rostering and shift scheduling, solved with state-of-the-art metaheuristics: Variable Neighbourhood Search, Evolutionary Ruin & Stochastic Recreate, and hybrid matheuristics combining metaheuristics with Integer Programming, evaluated against real-world data provided by BT.

Read the full thesis (PDF) →

Featured Talk

“The Factory Must Grow: Automation in Factorio”
Seminar for the BEACON Center for the Study of Evolution in Action, Michigan State University · 21,000+ views

Portfolio

Projects and interactive tools — the first four run in the browser, no setup

Blog

Technical write-ups and project deep-dives. View all posts →

Let's Collaborate

The fastest route is email: ken@kenreid.co.uk, or the contact page. New posts on data science land in the free newsletter. Profiles, for the diligent:

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