Christopher Bonnett
Machine Learning Engineer at Flo Health Inc.
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolChristopher Bonnett is a London-based machine learning engineer with 11 years of experience applying data-driven methods across academia and industry. He is currently building ML solutions at Flo Health after contributing to healthcare ML at ZOE from 2018 to 2024. His career blends rigorous astronomical data analysis and cosmology with practical ML deployment, including leading photometric redshift work for weak lensing in the Dark Energy Survey during his postdoc. An active open-source contributor, he improved the Edward probabilistic programming language's MDN implementation and created tutorials bridging Edward, Keras, and TensorFlow. He holds a PhD in Astronomy from Université Pierre et Marie Curie (cum laude) and an MSc in Astronomy from Leiden University, reflecting a strong foundation in data-intensive research. Based in London, he combines scientific rigor with production-focused ML engineering and a talent for making advanced models usable and educational.
11 years of coding experience
10 years of employment as a software developer
Msc, Astronomy, Msc, Astronomy at Leiden UniversityDoctor of Philosophy (Ph.D.), Astronomy and Astrophysics, Cum Laude (Mention: trés honorable), Doctor of Philosophy (Ph.D.), Astronomy and Astrophysics, Cum Laude (Mention: trés honorable) at Université Pierre et Marie Curie (Paris VI)
English, French, Dutch, Spanish
Github Skills (14)
tensorflow210
machine-learning10
probabilistic-programming10
deep-learning10
tensorflow10
neural-networks10
artificial-neural-networks10
deep-learning-ai10
data-science10
python9
bayesian-methods9
keras9
jupyter-notebook8
statistics8
Programming languages (4)
Github contributions (5)
blei-lab/edward
May 2016 - May 2016
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 4 PRs, 14 comments in 2 days
Contributions summary:Christopher's contributions focused on enhancing a Mixture Density Network (MDN) implementation within the Edward library. They refactored the training loop to separate prediction from training, improved code clarity by adding an empty line, and added a comprehensive notebook tutorial demonstrating MDN implementation with Edward, Keras, and TensorFlow. These changes indicate a focus on model optimization, usability, and educational support for MDN development.
inferenceneural-networksmachine-learningprobabilistic-programmingdeep-generative-models
cbonnett/SkyNet_wrapper
Aug 2014 - Oct 2014
Contributions:115 commits in 2 months
pythonskynetdeep-learningpython-wrappermachine-learning
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