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Graduate Teaching Assistant

King’s College London

• Deliver tutorials for undergraduate mathematics and labs for an interdisciplinary medical imaging module.
• Mark coursework and provide detailed feedback to cohorts of 60+ students.

Alpha Tech Intern – Quant Analysis

Man Group

• Designed experiments on simulated portfolios, enhancing Value-at-Risk calculations by incorporating earnings-announcement effects.
• Co-authored an internal research paper and delivered a 30-minute presentation to senior risk managers.
• Shipped production-ready Python code to calculate daily risk metrics.

Senior Consultant

Oxford Strategy Group Digital

• Open-sourced and extended Wise’s variational-autoencoder model for customer-behaviour prediction.
• Authored tutorials and Jupyter notebooks demonstrating applications for fintech clients.

Software Engineering Intern

Microsoft

• Built and documented an automated monitoring solution for critical infrastructure.
• Acted as technical lead among interns—wrote high-level design docs, test plans, and conducted code reviews.

Elected Student Representative

Smart Medical Imaging CDT

Organise research-development seminars and social events for the cohort.

PhD

King’s College London

Biomedical Engineering & Imaging Sciences

• Project: Fully Bayesian 3D PET-MR Neuroimaging Reconstruction.
• Investigating pre-trained diffusion models as generative priors for inverse problems in medical imaging.
• Supervisors: Prof. Andrew J. Reader and Prof. Andrew P. King.

MMathCompSci

St Anne’s College, University of Oxford

Mathematics & Computer Science

• First Class overall (77 %) and Distinction in final year (81 %).
• Master’s thesis: Improving the Security of Smartwatch Payment with Deep Learning.

Secondary School

John Mason School, Abingdon

A-Levels and EPQ

• A*A*A*A*A* in Mathematics, Further Mathematics, Computer Science, Physics, and an EPQ.

Likelihood-Scheduled Score-Based Generative Modeling for Fully 3-D PET Image Reconstruction

IEEE Transactions on Medical Imaging

Authors: G. Webber et al.

Steerable Conditional Diffusion for Domain Adaptation in PET Image Reconstruction

IEEE MIC 2025 (under review)

Authors: G. Webber et al. Submitted: May 2025

Distributional Consistency Loss: Solving Noisy Inverse Problems Without Overfitting

NeurIPS 2025 (under review)

Authors: G. Webber & A. J. Reader. Submitted: May 2025

Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction

IEEE Transactions on Radiation and Plasma Medical Sciences (under review)

Authors: G. Webber et al. Submitted: Feb 2025

Supervised Diffusion-Model-Based PET Image Reconstruction

MICCAI 2025 (under review)

Authors: G. Webber et al. Submitted: Feb 2025

Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Reconstruction

IEEE MIC 2024, Tampa (poster)

Authors: G. Webber et al.

Generative-Model-Based Fully 3-D PET Image Reconstruction by Conditional Diffusion Sampling

IEEE Medical Imaging Conference (MIC) 2024, Tampa (oral)

Authors: G. Webber et al.

Diffusion Models for Medical Image Reconstruction

BJR|Artificial Intelligence

Authors: G. Webber & A. J. Reader