Gogul Balakrishnan
Technical Lead at Google DeepMind
California, United States
Summary
🤩
Rockstar🎓
Top SchoolGogul Balakrishnan is a Technical Lead in California with a PhD in computer science from the University of Wisconsin–Madison and seven years of industry experience building ML, compiler and program-analysis driven developer tools. He has led GenAI initiatives at Google—shipping code-generation and developer-assist tools that were showcased company-wide—and now works at Google DeepMind. His hands-on work spans compilers and ML (contributions to Swift for TensorFlow and early fast.ai Swift experiments), production privacy tooling for Android’s Private Compute Core, and backend/devops optimizations for test toolchains. Earlier roles include building NEC’s Varvel static-analysis bugfinder and research on contextual code embeddings (CuBERT), reflecting a rare blend of program verification, automated testing, and large-scale systems engineering.
7 years of coding experience
11 years of employment as a software developer
Ph.D, Computer Science, Ph.D, Computer Science at University of Wisconsin-MadisonB.E, Computer Science, B.E, Computer Science at College of Engineering Guindy, Chennai
Github Skills (13)
tensorflow210
swift10
computer-vision10
deep-learning10
tensorflow10
data-processing9
differentiable-programming9
machine-learning8
unit-testing8
dockers7
test-automation7
docker7
dockerce7
Programming languages (9)
Github contributions (5)
tensorflow/swift-apis
Jun 2019 - Sep 2019
Swift for TensorFlow Deep Learning Library
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:60 commits, 125 PRs, 66 pushes in 3 months
Contributions summary:Gogul primarily contributed to the Swift for TensorFlow deep learning library by updating the codebase, fixing bugs, and refactoring code. They modified Dockerfiles to utilize the test tool chain and addressed failing tests. Furthermore, the user reorganized test files within the repository and added new requirements to the TensorGroup and TensorArrayProtocol. They also focused on performance by making adjustments to seed types and fixing style issues.
differentiable-programmingswift-for-tensorflowdeep-learningmachine-learningdeep-learning-library
fastai/fastai_dev
Apr 2019 - Aug 2019
fast.ai early development experiments
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 9 PRs, 7 pushes in 4 months
Contributions summary:Gogul contributed to the early development of a machine learning project, specifically focused on image processing and dataset manipulation within a Swift environment. They implemented image resizing functionalities using the Dataset API and made updates to data batching operations. The user also added helper functions for preparing data batches from image files, suggesting a focus on the data pipeline and model training preparation aspects.
pythondata-sciencemachine-learningfastainbdev
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