I’m the Research Manager at the Centre for the Governance of AI.

Previously, I was the course co-author and co-instructor for The Private AI Series Course 1, released in partnership with PyTorch, University of Oxford, & United Nations Global Working Group on Big Data. The course discusses data privacy and the competitive advantages of using privacy-preserving data analysis techniques such as federated learning, differential privacy, functional encryption, and multi-party computation.

I’m coauthor of a framework for thinking about information sharing called ‘structured transparency’, which outlines the components necessary to balance the use-misuse trade-offs of sensitive data.

I got into machine learning by building a neural network for studying brain age from brain MRI which is now published in Neuroimage. 

I completed my PhD in Medical Physics / Biomedical Engineering at the University of Oxford’s Institute of Biomedical Engineering, focusing on MRI physics, image analysis, & mathematical modelling. The 3 mathematical models I published from my PhD are available open-source on GitHub (1, 2, 3).
Here’s a summary of my PhD thesis.

I began my research career as a research assistant in groups working on mass spectrometry imaging of tumours at the University of Toronto, MRI for neuroscience at the Brain and Mind Institute, and MRI for lung research at the University of Western Ontario.

I’ve written blogs on data privacy, privacy-enhancing technologies, and machine learning for OpenMined and PyTorch. I enjoy painting in my spare time.