Gregory Chanan
Software Engineering Manager at Meta
New York, New York, United States
Summary
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Top School
Top expert inArtificial Intelligence and Machine Learning Technologies
Gregory Chanan is a Software Engineering Manager at Meta with 12 years of experience building high-performance ML infrastructure and production-grade libraries, notably as a long-term contributor and leader on PyTorch. He combines hands-on backend and CUDA expertise—demonstrated by substantial open-source contributions to Torch7, cutorch and cunn that improved GPU atomics, half-precision support, and broad test coverage—with people and program leadership spanning individual contributor to technical lead roles. Gregory’s background in numerical computing and optimization is rooted in Stanford MS/BS training and decades of low-level performance work, which helps him bridge research-grade models and robust production systems. Colleagues rely on him for pragmatic design trade-offs that keep large ML codebases fast, correct, and maintainable. An uncommonly test-focused engineer for a manager, he still contributes deep unit and integration improvements that reduce subtle GPU/CPU inconsistencies.
12 years of coding experience
14 years of employment as a software developer
MS Computer Science, MS Computer Science at Stanford University
Github Skills (27)
pytorch10
test-automation-framework10
c-language10
lib10
autotest-framework10
operation10
broadcasting10
atomics10
tensorrt10
gpu-programming10
machine-learning10
test-framework10
deep-learning10
tensorflow10
atomic10
Programming languages (10)
Github contributions (5)
torch/cunn
Aug 2016 - May 2017
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:98 commits, 23 PRs, 19 comments in 8 months
Contributions summary:Gregory primarily contributed to the improvement and extension of the testing framework for the `cunn` library, which includes CUDA accelerated neural network operations. They fixed an issue related to the behavior of the `nn.Clamp` module in CPU and GPU modes and added more comprehensive tests for several modules such as `HardTanh`, `Square`, `LogSoftMax`, `SpatialMaxUnpooling`, `SoftPlus`, `Tanh`, `LeakyReLU`, `SoftShrink`, `PReLU`, `RReLU`, `Sigmoid`, `Sqrt`, `Threshold`, `SpatialClassNLLCriterion`, `VolumetricMaxPooling`, `BCECriterion`, `MarginCriterion`, `SpatialUpSamplingBilinear`, `Abs`, `L1Cost`, `SpatialReflectionPadding`, `MultiLabelMarginCriterion`, `VolumetricReplicationPadding`, `SpatialFractionalMaxPooling`, `SpatialConvolutionLocal`, `TemporalMaxPooling`, and `SpatialAveragePooling`. They also made changes to enable more types of tensors to work with nn.Linear. Their work improved the coverage of unit and integration tests for the library's functionality and ensured it functions correctly across different tensor types.
torch/cutorch
Sep 2016 - Aug 2017
A CUDA backend for Torch7
Role in this project:Back-end Developer
Back-end DeveloperContributions:32 commits, 22 PRs, 28 comments in 10 months
Contributions summary:Gregory primarily contributed to the CUDA backend implementation for Torch7, focusing on extending the functionality and improving the performance of tensor operations. They added support for atomic indexAdd operations for various data types. Their work included modifying existing code and adding new test cases, specifically implementing functions using CUDA intrinsics and bitwise operations to improve atomicAdd performance. Furthermore, the user added support for half-precision floating-point tensors, demonstrating a focus on optimizing the library for GPU acceleration.
cudagpubackendcuda-backend
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