My research sits at the interface of statistics, mathematical modelling, decision science, and artificial intelligence, with a focus on building trustworthy, explainable tools for high-stakes decision-making.

I design and apply analytical methods that draw clear, actionable insight out of complex systems, combining the explainable strengths of statistical and physical models with novel computational techniques rather than relying on opaque "black box" approaches. A particular focus of my current work is verification and validation: making sure the models and tools we build actually do what we claim they do, and that their performance can be tested, trusted, and defended under scrutiny. This work has been applied across forensic science, intelligence, health, and psychology, and translates readily to any domain where decisions need to be accurate, defensible, and understood by the people who rely on them.

A core principle underpinning my approach is early and genuine engagement with end-users, the practitioners, analysts, and decision-makers who will ultimately use these tools, because that's what separates research that gets published from research that gets adopted. I lead multidisciplinary teams that take this work from concept to implementation, partnering with organisations that need rigorous, real-world solutions rather than theoretical ones.

If you're an industry partner or researcher exploring a potential collaboration, my track record spans complex, high-consequence problem domains, and I bring both the technical depth and the team leadership to see a project through from question to deployed, validated solution.