Kai Han is a software engineer with 11 years of experience who blends practical ML engineering with quantitative risk and finance experience across banks and securities firms. Based in New York, he has contributed to notable open-source AI backbone work (including a PyTorch GhostNet implementation) and brings hands-on model implementation and maintenance skills. Kai pairs this technical depth with domain knowledge from roles in credit risk analytics and investment banking, where he built automated monitoring systems and detection models that improved fraud prevention. He is completing advanced studies in enterprise risk management at Columbia, reflecting a rare combination of production ML skills and formal risk training. Colleagues describe him simply as "a happy coder" who thrives at the intersection of data, models, and real-world financial workflows.
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Role in this project:
ML Engineer
Contributions:7 releases, 2 reviews, 93 commits in 3 years 1 month
Contributions summary:Kai contributed to the development of efficient AI backbones, as indicated by the repository's description and topics. Their commits included modifications to file names, addition of licensing information, and code cleanup, indicating they were involved in project maintenance. A major contribution was the addition of a PyTorch version of the GhostNet model, as well as the addition of TinyNet and TNT code, which strongly suggests a focus on implementing and integrating different neural network architectures.
Contributions:20 commits, 19 pushes, 1 branch in 8 months
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