Xun Huang
Founder at Stealth Startup
Pittsburgh, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolXun Huang is a founder and PhD student in Computer Science at Cornell/Cornell Tech with ten years of experience building generative deep learning systems across industry and academia. He has held senior research scientist roles at NVIDIA and Adobe, led work on image/3D and video world models, and taught an advanced graduate course in deep generative models at CMU. His hands-on contributions to prominent open-source projects (e.g., NVlabs/MUNIT and NVIDIA's imaginaire) span core model architecture, training loop fixes and MLOps (Docker), including implementing a domain-invariant perceptual loss that improved robustness. Based in Pittsburgh, he is known for bridging cutting-edge research with production deployment and for turning complex generative ideas into reproducible code.
10 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Beihang UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Cornell UniversityExchange Student, Computer Science, Exchange Student, Computer Science at National University of Singapore
Chinese, English
Github Skills (13)
computer-vision10
pytorch10
image-translation10
docker10
mlops10
deep-learning10
cgan10
python10
dockers10
image-processing10
dcgan10
machine-learning9
tensorflow3
Programming languages (3)
Github contributions (5)
NVlabs/MUNIT
Mar 2018 - Oct 2020
Multimodal Unsupervised Image-to-Image Translation
Role in this project:
ML Engineer
ML EngineerContributions:58 commits, 2 PRs, 16 pushes in 2 years 7 months
Contributions summary:Xun contributed to the `nvlabs/munit` repository, which focuses on multimodal unsupervised image-to-image translation, by updating several Python files including `test.py`, `networks.py`, `trainer.py`, and `utils.py`. These updates suggest involvement in the core model architecture, training procedures, and testing scripts. The code changes include modifications related to network structures, training loops, and data loading, indicating the user's engagement in the model's implementation and refinement.
pytorchdeep-learningtranslationunsupervisedmunit
NVlabs/imaginaire
Sep 2020 - Oct 2020
NVIDIA's Deep Imagination Team's PyTorch Library
Role in this project:MLOps Engineer
MLOps EngineerContributions:7 commits, 1 PR, 7 pushes in 18 days
Contributions summary:Xun primarily contributed to the repository by modifying scripts related to building Docker images for the project, indicating involvement in the deployment and environment setup. They addressed a bug within the MUNIT trainer module and corrected a typo, suggesting a focus on refining model training and ensuring code quality. Furthermore, the user implemented domain-invariant perceptual loss, which directly enhances the model's robustness and performance.
pytorchnvidiaimage-synthesisimage-manipulationdeep-learning
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