Andrew Trask
Research Scientist
Oxford, England, United Kingdom
Summary
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RockstarAndrew Trask is a leader in privacy-preserving machine learning with 12 years of experience, combining roles as Leader of OpenMined, Senior Research Scientist at DeepMind, and PhD student at Oxford. He authored Grokking Deep Learning and teaches for Udacity, bridging accessible education with cutting-edge research. His open-source work—most notably on PySyft and Udacity’s private-AI course—focuses on differential privacy and PATE-style methods that enable data science while keeping data on remote servers. A Term Member at the Council on Foreign Relations with prior nonprofit board service, he pairs deep technical expertise with public engagement and community-driven stewardship.
12 years of coding experience
Github Skills (16)
sym10
web-framework10
pytorch10
machine-learning10
differential-privacy10
deep-learning10
python10
data-science10
web-frameworks10
numpy10
tensorflow9
mnist9
keras9
tensorflow29
notebook8
Programming languages (12)
Github contributions (5)
OpenMined/PySyft
Jul 2017 - Dec 2022
Perform data science on data that remains in someone else's server
Role in this project:
Data Scientist
Data ScientistContributions:6 releases, 302 reviews, 4130 commits in 5 years 5 months
Contributions summary:Andrew's contributions center around enhancing the SimpleService functionality within the PySyft project. Their work involved empowering simple-service messages to carry arbitrary payloads, improving the simple service's handling of messages, and fixing a bug related to database table creation for hagrid-launched Domains. This suggests a focus on extending the system's capabilities and fixing core functionality related to a specific, key service in the context of data science.
pytorchcryptographyacquiringpythonscience
iamtrask/Grokking-Deep-Learning
Aug 2016 - Feb 2020
this repository accompanies the book "Grokking Deep Learning"
Role in this project:Data Scientist
Data ScientistContributions:25 commits, 6 PRs, 27 pushes in 3 years 6 months
Contributions summary:Andrew primarily worked on implementing and refining deep learning models within the "Grokking Deep Learning" repository, focusing on chapter-specific code. Their contributions include code implementations related to chapter 10 and 13, along with modifications in chapter 14, showcasing a progression of learning and model refinement. The user appears to be actively engaged in exploring and implementing deep learning concepts and applying them to the MNIST dataset.
deep-learningpythondeep-neural-networksmachine-learning
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