Zach Eaton-Rosen is a Senior Research Engineer at DeepMind with a decade of experience bridging academic medical imaging and cutting-edge ML research to advance AGI. His background includes a PhD in Medical Image Computing from UCL, postdoctoral and research roles that produced imaging algorithms for neonatal MRI, and applied research scientist work at Graphcore focused on ML infrastructure. He has hands-on backend contributions to open-source medical imaging tooling (e.g., updating NiftyNet for Python 3), reflecting attention to robust, production-ready research code. Comfortable moving between theory and systems, he combines deep domain knowledge in medical imaging with practical expertise in high-performance ML engineering. Based in the UK, he brings a rare mix of teaching, academic rigor, and industry-scale research experience to complex, impactful AI projects.
10 years of coding experience
8 years of employment as a software developer
Master of Science (M.Sc.), Physics, II.i, Master of Science (M.Sc.), Physics, II.i at University of Cambridge
[unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy
Role in this project:
Back-end Developer
Contributions:107 commits, 9 PRs, 11 pushes in 2 years 4 months
Contributions summary:Zach contributed to the Python 3 compatibility of the NiftyNet platform, modifying existing code in the `network` and `inference` modules to ensure functionality with Python 3. They focused on updating core network functionalities, including the `HighRes3DNet` class, and the inference script, indicating a focus on the back-end systems and underlying infrastructure. Further changes in the `util.py` script suggest improvements to the system's core data loading capabilities. The commits indicate a general understanding of the project structure.
Contributions:28 pushes, 1 branch in 1 year 3 months
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Zach Eaton-rosen - Senior Research Engineer at Google DeepMind