Edward Loper
Scientist Engineer at BBN Technologies
Baltimore, Maryland, United States
Summary
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RockstarEdward Loper is a Scientist/Engineer with 24 years of experience based in Baltimore, currently applying deep expertise at BBN Technologies. He contributes to flagship open-source ML projects—most notably TensorFlow and Keras—focusing on practical improvements like modernizing API usage, strengthening ragged tensor support, and improving text-processing performance in tensorflow/text. His work blends hands-on engineering (refactoring layers, optimizing tokenizers, and adding shape functions) with clear technical writing for tensorflow/docs, showing he values both robust implementations and usable documentation. Colleagues rely on him to bridge research-grade models and production-ready tooling, often tackling subtle compatibility and performance issues that others overlook.
24 years of coding experience
Github Skills (11)
neural-network10
keras10
machine-learning10
deeplearning-ai10
nlp10
deep-learning10
tensorflow10
python10
documentation10
text-processing10
data-science9
Programming languages (4)
Github contributions (5)
tensorflow/text
Jun 2019 - Mar 2022
Making text a first-class citizen in TensorFlow.
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 1 branch, 7 comments in 2 years 9 months
Contributions summary:Edward primarily focused on enhancing the `tensorflow/text` library, which involves text processing in TensorFlow. Their contributions included updating test utilities to support RaggedTensors, adding shape functions for new operations, and improving the efficiency of the WordpieceTokenizer, including optimizing row partitioning and fixing documentation. These changes indicate a focus on refining core text processing functionalities within the TensorFlow ecosystem, specifically tailored to handle RaggedTensors.
tensorflow
tensorflow/docs
Apr 2020 - May 2022
TensorFlow documentation
Role in this project:Technical Writer
Technical WriterContributions:11 commits in 2 years
Contributions summary:Edward's contributions primarily focus on updating and expanding the documentation for the `tensorflow/docs` repository. They updated the "concrete_function" guide to reflect new features and behaviors, added content to the "ragged_tensor" guide, and corrected image links. The changes indicate a focus on providing comprehensive and up-to-date information for users of TensorFlow.
deep-learningmachine-learningdeep-neural-networkstensorflow-tutorialstensorflow
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