Edward Yang
Research Engineer at Facebook
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top School
Top expert inArtificial Intelligence and Machine Learning Technologies
Edward Yang is a research engineer with 17 years of experience bridging programming language research and production-grade machine learning infrastructure, currently based in Palo Alto and working at Facebook. He holds a deep academic background from MIT and Cambridge and completed a PhD-related stint at Stanford, with research roots in Haskell and compiler technology visible in contributions to GHC, Cabal, and build systems like Shake. In industry-grade ML, he has made substantive backend and DevOps contributions to core PyTorch projects—including pytorch/pytorch, pytorch/xla (TPU support), functorch, torchvision, and the PyTorch build system—focusing on tensor memory, dynamic-shape/autodiff integration, device compatibility and reproducible builds. His work marries low-level systems thinking (compiler/runtime, memory management, thread/runtime fixes) with practical tooling (CI/CD, packaging, cross-platform builds), and he’s comfortable navigating both research prototypes and production constraints. A less obvious strength is his long streak of precise bug fixes and build-system improvements that keep complex ecosystems shipping reliably across architectures and languages.
17 years of coding experience
Bachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at Massachusetts Institute of TechnologyComputer Science, Computer Science at University of Cambridge
Github Skills (76)
python10
operation10
multithreading10
sendmail10
package-management10
bash10
ruby10
e-mail10
type-system10
automation10
deep-learning10
template-haskell10
build-automation10
pip10
backend10
Programming languages (29)
Github contributions (5)
ezyang/htmlpurifier
Jul 2009 - Jan 2023
Standards compliant HTML filter written in PHP
Role in this project:
Back-end Developer
Back-end DeveloperContributions:56 reviews, 267 commits, 155 PRs in 13 years 9 months
Contributions summary:Edward primarily worked on back-end enhancements for the HTML Purifier project. Their contributions included addressing specific bugs related to incorrect URL munging and premature tag closing, improving the software's functionality and handling of malformed HTML. They also implemented support for various features, such as data URI schemes, YouTube slideshow embedding, and properties. Additionally, the user was involved in releasing new versions of the project.
compliantphphtml-parserstandardsfilter
pytorch/pytorch
Apr 2017 - Jan 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:Back-end Developer & AI/ML Engineer
Back-end Developer & AI/ML EngineerContributions:15535 reviews, 5681 commits, 5771 PRs in 5 years 10 months
Contributions summary:Edward primarily contributed to the PyTorch library, focusing on improvements related to the handling of tensor operations and dynamic shapes, specifically in the context of automatic differentiation and compiler optimizations. Their work involved enhancements to the memory management of tensors and the integration of automatic dynamic shape handling within the Inductor backend, as demonstrated by changes to support data-dependent tensor properties and optimizations for performance. They also worked on improving the support for unsigned integer data types within the library's core functionalities.
pythongpu-accelerationdeep-learninggpunumpy
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