Eric Kerfoot
Software Architect at King's College London
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolEric Kerfoot is a software architect and research software engineer with over a decade of experience applying mathematical modeling, 3D visualization and deep learning to biomedical challenges. Based at King’s College London, he leads efforts to improve research software quality, authors educational materials, mentors teams, and is a principal developer on Project MONAI—contributing practical ML tooling and tutorials used widely in medical imaging. His open-source work includes core updates to DLTK and MONAI tutorials and infrastructure, reflecting both model development (UNet, GANs) and project maintenance/DevOps practices such as testing and licensing. Comfortable bridging academia, clinical translation, and production, he has consulted on regulatory pathways for software-as-medical-device products and built cloud inference pipelines for surgical video analytics. Collectedly, his profile blends deep technical craft with a focus on reproducibility and long-term software stewardship in biomedical AI.
10 years of coding experience
10 years of employment as a software developer
DPhil (PhD), Computing (Software Engineering), DPhil (PhD), Computing (Software Engineering) at University of OxfordHBSc, Computer Science with Software Engineering Specialization, HBSc, Computer Science with Software Engineering Specialization at Western University
Github Skills (19)
unit-testing10
pytorch10
python10
dcgan10
machine-learning10
cgan10
mask-rcnn10
deep-learning10
tensorflow10
ai10
neural-network10
medical-imaging10
faster-rcnn10
jupyter-notebook10
cyclegan10
Programming languages (8)
Github contributions (5)
Project-MONAI/MONAI
Jan 2020 - Sep 2022
AI Toolkit for Healthcare Imaging
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:1102 reviews, 47 commits, 205 PRs in 2 years 8 months
Contributions summary:Eric's primary contribution was adding a license to every Python source file, which indicates a focus on project maintainability and legal compliance. This involved modifications across various modules within the project. The user also implemented a testing script and associated test cases. These actions suggest an engagement with project quality, code structure and automated processes, and basic DevOps skills.
pytorchpythondeep-learningmedical-image-processingcomputer-vision
Project-MONAI/tutorials
Jun 2020 - Aug 2022
MONAI Tutorials
Role in this project:ML Engineer
ML EngineerContributions:146 reviews, 12 commits, 60 PRs in 2 years 1 month
Contributions summary:Eric added a new tutorial notebook demonstrating the use of Generative Adversarial Networks (GANs) with the MedNIST dataset. This included adding generator and discriminator networks and an example notebook, along with code formatting. The code changes involved creating and modifying a Jupyter Notebook, suggesting the user's primary focus was on implementing and showcasing machine learning models within the MONAI framework. The notebook introduces and demonstrates a specific type of neural network.
pytorchmonai-tutorialsdeep-learningjupyter-notebookmonai
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