Hongyi Duan is a software engineer and quantitative researcher with 12 years of experience bridging data science, quant finance, and production engineering. Trained in finance (Nankai University) and pursuing an MS in Data Science at Duke, he has built automated attribution pipelines, alpha-factor mining frameworks, and LLM-based user profiling systems at firms including CICC and JD.com. He contributes to open-source projects across crypto (implementing SM2/SM4 in py-gmssl) and cloud-native tooling (automation and CI/CD for Drycc's Kubernetes workflows), reflecting a mix of backend, DevOps, and research skills. Proficient in Python, C++, SQL, R and advanced time-series, econometrics, and ML methods, he consistently turns noisy financial and behavioral data into actionable metrics and portfolios. Outside work he stays active in racket sports and team games, a pattern that mirrors his collaborative, iterative approach to problem solving.
12 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Data Science, Master of Science - MS, Data Science at Duke University
Bachelor's degree, Finance, General, 3.84/4, Bachelor's degree, Finance, General, 3.84/4 at Nankai University
The developer and operations friendly Kubernetes toolbox
Role in this project:
DevOps Engineer
Contributions:22 releases, 248 commits, 32 PRs in 3 years 11 months
Contributions summary:Hongyi primarily focused on automating the deployment and management of the Drycc workflow project. Their contributions involved creating and modifying installation scripts, particularly for k3s, and configuring core components like Cilium, Longhorn, Traefik, and service catalog. The user also updated the project's dependencies and configuration for CI/CD pipelines, demonstrating a focus on infrastructure-as-code and streamlined deployment processes. They addressed issues related to storage, networking, and certificate management.
Contributions:22 commits, 17 PRs, 26 pushes in 4 years 2 months
Contributions summary:Hongyi primarily focused on adding and improving cryptographic functionalities within the `py-gmssl` repository. Their contributions included implementing SM2 and SM4 cryptographic algorithms, adding corresponding documentation, and refactoring code to improve its maintainability. The user also introduced testing capabilities by adding tests for SM2 and SM3, alongside fixing bugs. The commits demonstrate a focus on expanding the library's capabilities related to Chinese cryptographic standards.
pythonrsasm2cryptanalysissm3-sm4
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