About

I’m currently a software engineer at a legal tech startup building tools for turning unstructured data into structured data. Before this, I was a machine learning engineer at a startup building an AI-powered data analytics platform.

Before entering industry, I was a university lecturer and researcher in cognitive neuroscience, philosophy, and applied statistics. My research examines the conceptual, statistical, and computational tools used in cognitive science: how we explain cognition, what counts as evidence, and how to make research reproducible.

I hold a PhD in Cognitive Science from Macquarie University in Sydney, and I have held academic positions at the University of Sussex, the University of Cambridge, and the Donders Institute for Brain, Behaviour, and Cognition at Radboud University (Nijmegen, Netherlands).

I’m particularly interested in the foundations of statistical inference, including Bayesian and frequentist approaches, and in the role of representation and computation in explanations of the mind. My work has also examined computational reproducibility in psychology and the environmental impact of research computing. You can find more on the research and publications pages.

I develop open-source tools for statistical analysis, including Bayesplay, an R package and web app for constructing Bayesian models and computing Bayes factors. Alongside this work, I’ve taught research methods, Bayesian statistics, and statistical programming with R. My software and teaching materials are available online.

Selected publications

CATS: The Climate Aware Task Scheduler (2025), published in Journal of Open Source Software. Read the paper.

Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging (2023), published in Imaging Neuroscience. Read the paper.

What’s in a Badge? A Computational Reproducibility Investigation of the Open Data Badge Policy in One Issue of Psychological Science (2023), published in Psychological Science. Read the paper.