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Home > Ezra Webb

Ezra Webb

Machine Learning Engineer

Role at Trilateral

Ezra Webb is a Machine Learning Engineer currently working on the development of the CESIUM application; aiming to develop better methodologies for identifying risk of child exploitation using data science and machine learning. Ezra is particularly interested in the development of model-agnostic algorithmic transparency and explainability techniques for elucidating the decision-making processes of machine learning models. This is in order to provide practitioners with explanations and insights they can use to effectively audit, verify, contextualise, and potentially falsify, a model’s outputs, thus enabling human-focused machine-assisted decision-making, as opposed to prescriptive autonomous decision-making. 


Ezra has a background in deep learning and physics, and completed his MSci in Physics at the University of Cambridge, where he also earned a BA in Natural Sciences. His master’s thesis was based on using deep learning and graph neural networks to develop models for complex physical dynamical systems in an unsupervised way. 

He has also conducted research projects, involving deep learning, computer vision and large-scale Monte Carlo simulations, at the University of Tokyo, the California Institute of Technology, the Rutherford-Appleton Laboratory, and the Cavendish Laboratory. 

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