William Woof
Chief AI Officer at Eye2Gene
United Kingdom
Summary
👤
Senior🎓
Top SchoolWilliam Woof is Chief AI Officer and co-founder of Eye2Gene, leading development of clinical-grade AI models to accelerate therapies for rare genetic eye diseases. With 11 years in AI research and a PhD in autonomous learnable video-game AI from the University of Manchester, he combines deep technical expertise—including contributions to Tensor2Tensor and Transformer architectures—with translational research experience from a five-year fellowship at UCL Institute of Ophthalmology. Living with Retinitis Pigmentosa himself, he brings personal urgency to building trustworthy, actionable ML tools that bridge research and patient impact. His background spans deep reinforcement learning, generative models, and practical model engineering, evidenced by implementing custom attention and masking improvements in a prominent open-source library. Based in the UK, he blends academic rigor, open-source contributions, and startup leadership to move novel therapies toward patients.
11 years of coding experience
Doctor of Philosophy - PhD, Autonomous Learnable Video-game AI, Doctor of Philosophy - PhD, Autonomous Learnable Video-game AI at The University of ManchesterMaster's degree, Mathematics, Master's degree, Mathematics at Durham University
Github Skills (8)
transformer10
machine-learning10
deep-learning10
tensorflow10
python9
machine-translation8
reinforcement-learning6
tpu5
Programming languages (7)
Github contributions (5)
tensorflow/tensor2tensor
Jul 2017 - Aug 2017
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 2 PRs, 6 comments in 21 days
Contributions summary:William contributed to the development of an alternative Transformer network architecture within the tensor2tensor library. Their work included adding new layer types and modifying existing components. The user's commits demonstrate an understanding of the Transformer model and its implementation, including encoder and decoder structures. They implemented changes to masking and attention mechanisms.
pytorchautoencoderdeep-learningmachine-translationreinforcement-learning
EndingCredits/Neural-Episodic-Control
May 2017 - May 2018
Contributions:40 commits, 1 PR, 31 pushes in 11 months
deep-learningdeepmindepisodic-control
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