Haoyu Zhang
Software Engineer at Meta
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHaoyu Zhang is a software engineer with 12 years of experience, currently working on Android and Jetpack at Google from the heart of Silicon Valley. He brings deep systems and ML runtime expertise—his open-source contributions to TensorFlow (including TFRT and core runtime changes) show a focus on distributed execution, data-race-free collective kernels, and multi-process gRPC communicator testing. Haoyu pairs backend and DevOps skills with model-level optimizations (ResNet/Keras performance tuning and XLA integration), enabling end-to-end performance improvements across training and serving. That blend of mobile platform engineering and low-level ML systems work is uncommon and lets him bridge device-side concerns with distributed compute efficiency.
12 years of coding experience
12 years of employment as a software developer
Exchange Student Computer Science, Exchange Student Computer Science at Technion - Israel Institute of TechnologyBachelor’s Degree Computer Science, Bachelor’s Degree Computer Science at Peking UniversityDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Princeton University
Chinese, English
Github Skills (25)
c-language10
back-end-development10
python10
graph10
machine-learning10
distributed-systems10
keras10
tensorflow10
gpu10
resnet10
optimisation10
cprogramming-language10
grpc10
optimization10
image-classification9
Programming languages (5)
Github contributions (5)
tensorflow/models
Jan 2019 - Dec 2019
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:63 commits, 104 PRs, 134 pushes in 11 months
Contributions summary:Haoyu primarily focused on optimizing and enhancing the performance of Keras-based ResNet models within the TensorFlow framework. Their contributions included implementing data format adjustments for improved GPU performance and fine-tuning graph compilation for efficient training. They also added flags to enable XLA for accelerated computation and integrated XLA tests, alongside fixing lint errors and refactoring code to improve model performance and efficiency. The user added code to reduce cross-device overhead.
deep-learningtensorflow
tensorflow/tensorflow
Jan 2019 - Dec 2022
An Open Source Machine Learning Framework for Everyone
Role in this project:Back-end Developer
Back-end DeveloperContributions:11 reviews, 233 commits, 1 PR in 3 years 11 months
Contributions summary:Haoyu's contributions primarily involve changes to the core TensorFlow runtime and distributed execution components. They modified the `ProcessFunctionLibraryRuntime` and `WorkerSession` classes, indicating work on function instantiation, distributed computation, and graph optimization. The commits demonstrate involvement in publishing subgraphs, managing session states, and integrating with coordination services within the TensorFlow framework. These changes suggest a focus on enhancing the execution efficiency and distributed capabilities of the machine-learning framework.
pythondata-sciencedeep-learningmlmachine-learning
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