Peiyuan Liao
Founding Member Of Technical Staff at Condé Nast
United States
Summary
🤩
Rockstar🎓
Top SchoolPeiyuan Liao is a founding member of technical staff and machine learning engineer with eight years of experience building foundation and multi-modal models, advising on AI and cloud architectures, and scaling product teams from prototype to 50+ engineers. He blends research pedigree from Carnegie Mellon (collaborating with leaders like Tianqi Chen and Ruslan Salakhutdinov) with hands-on product and infrastructure impact—CTO experience in consumer electronics and contributions to optimized graph algorithms in the popular pytorch_cluster extension. A Kaggle Competitions Grandmaster since his teens, he pairs competitive modeling instincts with production-grade software development and test automation. Currently advising Condé Nast and Terroir Research while helping launch Project Prometheus, he moves comfortably between strategy, research, and implementation. Notably, his GitHub motto “amor fati” reflects a pragmatic, resilient engineering approach that favors code quality and rigorous testing.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy Computer Science, Doctor of Philosophy Computer Science at Stanford UniversityBachelor of Science Computer Science, Bachelor of Science Computer Science at Carnegie Mellon UniversityHigh School Mathematics and Computer Science, High School Mathematics and Computer Science at Kent School
Chinese, English, French
Github Skills (7)
pytorch10
python10
testing10
graph-neural-network9
geometric-deep-learning9
c-language8
cprogramming-language8
Programming languages (13)
Github contributions (5)
rusty1s/pytorch_cluster
May 2020 - Jun 2020
PyTorch Extension Library of Optimized Graph Cluster Algorithms
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:17 commits, 1 PR, 22 comments in 1 month
Contributions summary:Peiyuan contributed to the project by fixing various errors and improving the code quality, specifically addressing Flake8 style issues to ensure code consistency. Their work involved modifying Python files, particularly within the `torch_cluster` directory. The user also implemented and updated several tests to validate the functionality of the implemented algorithms, ensuring correctness across various dimensions and configurations.
pytorchgeometric-deep-learningcluster-algorithmsmachine-learninggraph-neural-networks
liaopeiyuan/GAL
Sep 2020 - Jul 2021
Graph Adversarial Networks: Protecting Information against Adversarial Attacks
Contributions:1 review, 9 commits, 1 PR in 9 months
pytorchadversarial-attackssocial-network-analysisdataminingdeep-learning
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