This site presents work at the intersection of systems engineering, analytics and applied AI, with a focus on building usable solutions that perform well in real operational contexts.
The emphasis is not on isolated tools or coding exercises. It is on analytical and AI-enabled systems shaped by requirements, workflows, users, constraints and deployment context.
Systems Data Science combines systems thinking, systems engineering and applied data science to develop useful, responsible and operationally effective solutions.
It treats analytics and AI as components within wider systems of people, processes, information, technology and governance. The focus is not only on technical performance, but on whether solutions meet real user needs, fit operational workflows, manage risk and deliver meaningful outcomes.
Applied AI and workflow tools
AI-enabled tools developed or trialled to support users within real workflows, with attention to usability, performance testing, governance and practical adoption.
Operational analysis
Analytical methods applied to planning, prioritisation, forecasting, performance improvement and other practical operational problems.
Simulation and optimisation
Models used to understand system behaviour, evaluate options, test assumptions and improve performance under constraints.
Systems engineering and technical assurance
Work grounded in requirements, system context, risk, interoperability, safety and technical credibility across complex environments.
This portfolio brings together selected public work and supporting material across AI-enabled systems, applied analytics, simulation and optimisation, including repositories and project summaries spanning a range of technical interests and methods.
Projects are presented with an emphasis on:
Selected technical notes, short written pieces and references to conference presentations where relevant.
These are intended to show how I think about systems, analytics, AI-enabled tools, workflow integration and practical performance in real settings.
Coming soon…Read about technical notes and ideas
Repositories and materials which reflect earlier exploratory work and technical experimentation.
They are retained as historical context, illustrating how technical interests and methods have evolved over time, particularly in areas such as machine learning.