Chiyuan Zhang
Research Scientist at Google Brain
Boston, Massachusetts, China
Summary
🤩
Rockstar🎓
Top School
Top expert inHigh-Performance Machine Learning Computing
Chiyuan Zhang is a research scientist at Google Brain with 18 years of software and ML experience, blending deep academic training (PhD from MIT) with long-standing contributions to open-source deep learning and tooling. His work spans low-level infrastructure and applied ML, with notable contributions to high-profile projects like MXNet and Caffe as well as Julia-native frameworks (Mocha.jl), demonstrating comfort across languages and runtimes. He has a history of impactful internships and research at DeepMind, Google, and MIT, applying machine learning to practical domains including geological data analysis. Equally at home in backend systems and algorithmic model work, he has fixed edge-case bugs, extended data-layer functionality, and implemented core features in multiple libraries. Based in Boston with roots in China, he pairs rigorous research instincts with pragmatic engineering—often focusing on the plumbing that makes scalable ML reproducible and robust. A less obvious thread through his career is a recurring interest in language runtimes and developer ergonomics, shown by contributions to Emacs/YASnippet and VM/FFI work.
18 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at MITSummer Exchange Program, Computer Science, Summer Exchange Program, Computer Science at Kyoto UniversityBE, Computer Science, BE, Computer Science at Zhejiang University
Chinese, English, Japanese
Github Skills (44)
hdf10
sgd10
c-language10
caffe10
gd10
python10
data-science10
api-design10
testing10
machine-learning10
virtual-machine10
template-engine10
templater10
ruby10
machine-vision10
Programming languages (14)
Github contributions (5)
pluskid/Mocha.jl
Oct 2014 - Dec 2018
Deep Learning framework for Julia
Role in this project:
ML Engineer
ML EngineerContributions:885 commits, 85 PRs, 212 pushes in 4 years 2 months
Contributions summary:Chiyuan made significant contributions to a deep learning framework for Julia. Their commits focused on implementing and integrating various features, including removing dependencies, adding momentum for stochastic gradient descent, implementing a basic weight initializer, making the backend explicit, adding regularizers, and implementing a softmax loss layer. These contributions indicate a focus on the core functionality and optimization of the framework, along with building out the layer capabilities.
automatic-differentiationdeep-learningmachine-learningneural-networkframework-learning
apache/mxnet
Oct 2015 - May 2017
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:Back-end Developer
Back-end DeveloperContributions:699 commits, 109 PRs, 43 pushes in 1 year 7 months
Contributions summary:The user, pluskid, contributed to the development of the MXNet.jl project by focusing on the low-level infrastructure. Their work included generating files for Julia, implementing basic API testing, creating and manipulating NDArrays, and resolving code typos. Their contributions primarily involved building and maintaining the core components of the library.
pythonschedulerdataflowmutationdata-science
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