Xuan Kan is a Research Scientist at Meta with a decade of experience applying machine learning research to production problems, currently focusing on Auto Eval and post-training for Monetization GenAI. He holds a Ph.D. in Computer Science from Emory University and has a strong track record of internships and research roles at Meta, Google, and the University of Oxford that bridge federated learning, ASR efficiency, and ads signal-loss challenges. Xuan has designed production models (e.g., Mobile App Install model) and novel architectures like MTML Hypergraph Neural Networks to tackle multi-task, multi-label problems in advertising. His work combines rigorous academic publication experience with hands-on systems engineering, and he often targets communication- and data-efficiency in large-scale ML deployments. Based in Menlo Park, he brings both deep research credentials and proven product impact across industry-scale ML systems.
10 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at Tongji University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Emory University
A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”
Contributions:146 pushes in 5 months
pytorchextractiondeep-learningcvpr-2020training
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