Allen Goodman
Senior Principal Machine Learning Engineer at Genentech
Somerville, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolAllen Goodman is a Senior Principal Machine Learning Engineer with 11 years of experience building production-ready computer vision and ML systems, now leading ML efforts at Genentech after research roles at Meta and the Broad Institute. His work spans from microscopy image analysis—co-authoring CellProfiler—to foundational contributions in PyTorch (special function kernels) and Keras-RCNN object detection modules, blending deep mathematical rigor with practical engineering. At Meta AI he focused on data compression, optical neural networks, and differentiable systems, bringing research-forward techniques into applied pipelines. Comfortable across CUDA kernels, Python ecosystems, and imaging toolkits, he combines academic curiosity (Amherst mathematics) with a track record of shipping robust open-source and enterprise software. An uncommon thread through his career is translating advanced math into performant, maintainable code that scales from lab-grade imaging to large ML platforms.
11 years of coding experience
15 years of employment as a software developer
Mathematics, Mathematics at Amherst College
Stackoverflow
1reputation
0reached
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Github Skills (24)
pytorch10
python10
image-processing10
machine-learning10
scikit-image10
math10
maths10
math-functions10
keras10
deep-learning10
tensorflow10
cuda10
object-detection10
computer-vision10
tensor10
Programming languages (12)
Github contributions (5)
broadinstitute/keras-rcnn
Apr 2017 - May 2020
Keras package for region-based convolutional neural networks (RCNNs)
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:2 releases, 483 commits, 98 PRs in 3 years 1 month
Contributions summary:Allen primarily contributed to the Keras-RCNN project by implementing features related to bounding box regression and object detection within the RPN and RCNN models. They demonstrated expertise in the development of machine learning loss functions, including the Smooth L1 loss and categorical cross-entropy, key components for training object detection models. Additionally, the user made incremental code updates, refactoring, and debugging to improve the performance and functionality of the core modules.
keras-modelsdeep-learningimage-segmentationtheanoobject-detection
CellProfiler/CellProfiler
Aug 2015 - Sep 2020
An open-source application for biological image analysis
Role in this project:Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:16 releases, 6 reviews, 819 commits in 5 years 1 month
Contributions summary:Allen's commits primarily focused on refining image processing functions within the CellProfiler project. They implemented a morphological skeleton and utilized the skimage.morphology library. The user also made efforts to ensure compatibility with Python 3 and integrated the scikit-image library for diverse image processing operations.
image-analysispythonneuroimagingbiologicalmicroscopy
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