David Carter
Mathematics Tutor
Cambridge, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolDavid Carter is a Cambridge-educated machine learning and statistics engineer with over a decade of experience applying deep learning to medical imaging, speech, NLP, synthetic biology and genomics for organisations including Microsoft, Google, NASA, the Sanger Institute and SRI International. He combines a PhD in computer science and a first-class maths degree with hands-on MLOps and production deployment experience—most recently improving AzureML workflows for Microsoft’s InnerEye 3D medical segmentation stack. Now based in Cambridge, he builds a one-to-one A-level and Further Maths tutoring practice while remaining open to remote, mission-aligned ML consulting. His career blends academic publications and high-impact industry products, and he prefers work that delivers clear human or environmental benefit rather than purely financial or military objectives.
10 years of coding experience
32 years of employment as a software developer
PhD Computer Science, PhD Computer Science at University of CambridgeKing's School Worcester
Github Skills (14)
continuous-deployment10
azure-pipelines10
azure10
deep-learning10
microsoft-azure10
azure-machine-learning10
python10
ml-deployment10
cicd10
modeling9
trainings9
pytorch9
docker8
dockers8
Programming languages (5)
Github contributions (5)
microsoft/InnerEye-DeepLearning
Jul 2020 - Aug 2020
Medical Imaging Deep Learning library to train and deploy 3D segmentation models on Azure Machine Learning
Role in this project:
MLOps Engineer
MLOps EngineerContributions:13 commits in 1 month
Contributions summary:David primarily focused on improving the Azure Machine Learning (AML) deployment and operational aspects of the deep learning library. They modified the code to ensure all run outputs are visible in AML, and enhanced the model summary logging. The user also introduced a script to submit an AzureML job for running a model on an image, and improved cross-validation results presentation. In addition, they updated dependencies, and reorganized output directories to streamline the model building and ensemble creation process.
deep-learningmachine-learningazure-machine-learningdeep-learning-libraryimaging
sennendoko/emukit
Jun 2021 - Aug 2021
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Contributions:12 pushes, 5 branches in 1 month
pythonparameter-estimationsensitivity-analysistoolboxstatistical-inference
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.