Tongzhou Wang
Member Of Technical Staff at OpenAI
United States
Summary
🤩
Rockstar🎓
Top SchoolTongzhou Wang is an AI researcher and software engineer with 11 years of experience, currently a Member of Technical Staff at OpenAI after completing a PhD in Machine Learning at MIT CSAIL. He has deep hands-on expertise in PyTorch and computer vision, contributing code and documentation to flagship projects like pytorch/pytorch and torchvision as well as influential repos for dataset distillation and CycleGAN/pix2pix. His background spans both research and production—internships and roles at Meta/Facebook, Airbnb, and UC Berkeley—and he’s contributed performance and memory fixes that improve real-world model training and inference. Notably, he has worked on dataset distillation training loops and data-loading optimizations, a niche that bridges efficient ML research and engineering for large-scale workflows.
11 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.) Computer Science Statistics, Bachelor of Science (B.S.) Computer Science Statistics at University of California, BerkeleyDoctor of Philosophy Machine Learning, Doctor of Philosophy Machine Learning at Massachusetts Institute of TechnologyMiddle School & High School, Middle School & High School at Shanghai Foreign Language SchoolComputer Science; Electrical and Computer Engineering, Computer Science; Electrical and Computer Engineering at Carnegie Mellon University
Github Skills (24)
pytorch10
develop10
python10
image-processing10
machine-learning10
generative-adversarial-network10
deep-learning10
computer-vision10
documentation10
algorithm9
code-optimization9
algorithms9
tensorflow9
optimisation9
numerical-optimization9
Programming languages (14)
Github contributions (5)
ssnl/dataset-distillation
Dec 2018 - May 2022
Dataset Distillation
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 26 commits, 5 PRs in 3 years 6 months
Contributions summary:Tongzhou primarily contributes to the `base_options.py` file, adding and modifying arguments related to the training process, and model architecture, including dropout, distillation parameters, and learning rates. Several commits focus on improving the data loading and transformation process. Further contributions involve modifications to the distillation training loop within `train_distilled_image.py`, and fixing issues related to optimizing distilled image generation and memory usage.
deep-learningpytorchdatasetdistillation
pytorch/pytorch
Sep 2017 - Oct 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:Technical Writer
Technical WriterContributions:13 reviews, 506 commits, 653 PRs in 5 years 1 month
Contributions summary:Tongzhou primarily contributed to the project by updating and improving the documentation. Their commits focused on fixing grammar issues, correcting math displays, adding missing colons, and improving code examples within the documentation files. The user also added documentation for new features like `weight_norm` and refined sections related to extending and using the PyTorch framework, enhancing the overall clarity and usability of the documentation.
pythongpu-accelerationdeep-learninggpunumpy
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