Jeff Donahue
Research Scientist at DeepMind
England, United Kingdom
Summary
🤩
Rockstar🎓
Top School
Top expert inDeep Learning and Computer Vision Technologies
Jeff Donahue is a Research Scientist at DeepMind with 12 years of experience at the intersection of machine learning and computer vision, grounded by a Ph.D. from UC Berkeley and a Turing Scholars BS from UT Austin. He combines rigorous academic research with production-focused engineering, having interned and worked on image and search systems at Google and built infrastructure and backend features for Pinterest. His open-source contributions include build and core fixes to the widely used Caffe2 deep learning framework, demonstrating low-level systems fluency alongside high-level model development. Based in England, he brings practical DevOps and build-system expertise to research projects, ensuring experiments scale and reproduce. Known for addressing subtle correctness issues (e.g., memcpy argument orders, empty-dimension tensors), he blends meticulous code hygiene with ambitious research goals.
12 years of coding experience
6 years of employment as a software developer
B.S., Turing Scholars Honors Computer Science, B.S., Turing Scholars Honors Computer Science at The University of Texas at AustinPh.D., Computer Science, Ph.D., Computer Science at University of California - Berkeley
Github Skills (8)
machine-learning10
c-language10
deep-learning10
caffe10
cprogramming-language10
python9
cuda6
tensorflow5
Programming languages (5)
Github contributions (5)
facebookarchive/caffe2
Jul 2015 - Jul 2015
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:20 commits in 6 days
Contributions summary:Jeff primarily worked on build environment configurations and core framework functionalities within the Caffe2 deep learning framework. Their contributions included modifying build scripts to include necessary directories for include and library files, as well as adding python library directories to the build process. Furthermore, the user addressed a copy/memcpy function argument order and other code changes to improve core functionality. The user's work also involved modifications to allow tensors with empty dimensions and adapt to the framework's operator behavior.
pytorchscalablecaffe2deep-learningml
jeffdonahue/caffe
Apr 2014 - Jan 2016
Contributions:356 commits, 3 PRs, 345 pushes in 1 year 9 months
caffe
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.