George Strong is a Senior Generative AI Engineer with seven years’ experience applying ML and deep generative models to healthcare and scientific problems, currently building generative AI solutions at all.health. Trained as a computational physicist with a PhD in machine learning for ultrasound neuroimaging from Imperial College London, he blends rigorous numerical simulation and PDE expertise with practical GPU-accelerated software engineering (CUDA, distributed GPU clusters). He has led teams to productionize ML systems—delivering a 5× CUDA speed-up for imaging pipelines and reducing ultrasound model errors from hundreds of percent to low double digits—while also developing cloud-native MLOps and microservices. A practiced educator and communicator, he taught diffusion models and ML to master’s students and trained data science professionals across Europe, which sharpens his ability to translate research into impactful products. Comfortable across Python, C++, Go, PyTorch, Ray and Kubernetes, he focuses on fine-tuning, RAG/compound AI systems, and production inference at scale. Outside core AI work he brings an unusual mix of musical training and outreach experience, reflecting a collaborative, interdisciplinary approach to problem solving.
7 years of coding experience
6 years of employment as a software developer
Module S176 | Living without oil: chemistry for a sustainable future, Module S176 | Living without oil: chemistry for a sustainable future at The Open University
Doctor of Philosophy (PhD), Machine learning for ultrasound neuroimaging, Doctor of Philosophy (PhD), Machine learning for ultrasound neuroimaging at Imperial College London
Sixth Form, Sixth Form at Wymondham College
Imperial Horizons: An Introduction to Management, BUSINESS, MANAGEMENT, MARKETING, AND RELATED SUPPORT SERVICES, Distinction, Imperial Horizons: An Introduction to Management, BUSINESS, MANAGEMENT, MARKETING, AND RELATED SUPPORT SERVICES, Distinction at Imperial College Business School
Bachelor's of Music (BMus), Bachelor's of Music (BMus) at Royal College of Music
Machine Learning | Coursera, Computer Science, Machine Learning | Coursera, Computer Science at Stanford University
Contributions:8 PRs, 75 pushes, 5 branches in 1 month
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