David Warde-farley
Staff Research Scientist at Google DeepMind
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolDavid Warde-farley is a Staff Research Scientist at Google DeepMind with 17 years of experience building and improving machine learning systems and developer tooling. He holds a PhD in Computer Science and has progressed through multiple research scientist roles at DeepMind after internships at Google Brain and image understanding teams. His work blends deep research with pragmatic engineering—contributing to core scientific Python projects (NumPy, SciPy, scikit-learn) and foundational ML libraries like Theano, Pylearn2, Fuel and Hyperopt. He combines back-end systems, test automation, and UX-savvy front-end fixes (notably enhancing Jupyter/IPython notebook ergonomics) and often focuses on code quality and documentation to make complex tools more usable. Colleagues describe him as someone who moves seamlessly between research-grade models and the gritty maintenance that keeps open-source infrastructure reliable.
17 years of coding experience
6 years of employment as a software developer
PhD, Computer Science, PhD, Computer Science at Université de MontréalHon. B. Sc., Computer Science, Hon. B. Sc., Computer Science at University of Toronto
Github Skills (58)
algorithm10
unit-testing10
algorithms10
notebook10
javascript10
restructuredtext10
data-pipelines10
jupyter10
python10
data-science10
sphinx10
scikit10
testing10
technical-writing10
machine-learning10
Programming languages (13)
Github contributions (5)
lisa-lab/pylearn2
Jul 2010 - Jun 2015
Warning: This project does not have any current developer. See bellow.
Role in this project:
ML Engineer
ML EngineerContributions:991 commits, 31 PRs, 10 pushes in 5 years
Contributions summary:David made several contributions related to the implementation of Restricted Boltzmann Machines (RBMs) and autoencoders within the pylearn2 framework. The commits include code for constructing RBMs with Gaussian-binary visible units, adding support for the identity activation function, implementing the stochastic gradient descent algorithm, defining and improving the performance of training for those networks, and other model building and testing functionalities. The user's work also involved the refactoring and enhancement of various modules, including those for building and using Deep Belief Networks.
javascripttypescript
mila-iqia/blocks
Feb 2015 - Nov 2016
A Theano framework for building and training neural networks
Role in this project:Back-end Developer
Back-end DeveloperContributions:306 commits, 174 PRs, 266 pushes in 1 year 9 months
Contributions summary:David primarily contributed to the Theano framework for building and training neural networks, focusing on improvements to the SimpleExtension class, including the addition of features like `every_n_epochs` and the handling of invalid condition keywords. They also addressed flake8 errors, refactored code, and made modifications to testing files by setting up the right conditions for model evaluation and serialization of roles. Furthermore, they added support for Theano known_grads and parameter clipping, improving the functionality of the GradientDescent class and algorithm performance.
pytorchdeep-learningtheanoneural-networksmachine-learning
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.