Guanheng Zhang
Research Scientist Facebook AI Research Instagram
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolGuanheng Zhang is a research scientist at Facebook AI Research for Instagram, bringing 11 years of experience applying deep learning and NLP to large-scale recommendation systems and production features like Instagram Shopping on Explore. He combines a Ph.D. in computational nuclear reactor physics with hands-on software development, enabling rigorous experimentation, uncertainty quantification, and scalable model deployment. Prior work at Argonne National Laboratory includes building analysis toolkits and optimization pipelines for advanced reactor safety, reflecting strong foundations in scientific computing and large-data engineering. An active PyTorch contributor, he has improved text-dataset tooling and added APIs for QA and BERT examples, bridging open-source NLP tooling with production research. Based in New York, he is comfortable leading cross-functional teams and translating complex research into robust, production-ready systems.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computational Physics in Nuclear Reactor, Doctor of Philosophy (Ph.D.), Computational Physics in Nuclear Reactor at University of California, BerkeleyB.S.E., Nuclear Engineering and Radiological Science, B.S.E., Nuclear Engineering and Radiological Science at University of Michigan
Github Skills (9)
pytorch10
nlp10
python10
text-classification10
datasets10
data-engineering9
machine-learning9
data-structures8
data-structure8
Programming languages (5)
Github contributions (5)
pytorch/text
Jun 2019 - Mar 2021
Models, data loaders and abstractions for language processing, powered by PyTorch
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:10 releases, 167 reviews, 165 commits in 1 year 8 months
Contributions summary:Guanheng primarily worked on the PyTorch text processing library, contributing to the development of new datasets for text classification. Their contributions included implementing features for building datasets, such as functions to download and extract archives, as well as generating n-grams. Additionally, the user updated and improved text normalization functions and documentation to improve dataset usage. The user also worked on the Question Answering and the BERT example by adding the related APIs.
nlppytorchloadersdeep-learningdataset
zhangguanheng66/pytorch
Mar 2019 - Nov 2020
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:302 pushes, 78 branches in 1 year 8 months
pythongpu-accelerationdeep-learninggpuacceleration
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