Honggang Liu
Financial Risk Analyst at Shanghai Electric Group Co., Ltd
Virginia, United States
Summary
👤
Senior🎓
Top SchoolHonggang Liu is a Financial Risk Analyst with 11 years of cross-disciplinary experience blending finance, commodity research, and MLOps engineering. Currently at Shanghai Electric and an MS Finance student at George Washington University, he has built proprietary commodity databases and valued pre-production mining projects to support trading and investment decisions. He also brings strong hands-on open-source engineering experience, contributing to high-profile Kubernetes ML projects like Kubeflow and KServe—improving Katib study job controllers, CI/CD, and deployment orchestration for production ML workflows. Earlier roles at Minmetals, IBM, KPMG and investment banking internships give him a practical edge in financial strategy, audit and IPO processes. Comfortable bridging quantitative finance and cloud-native ML infrastructure, he excels at turning complex technical and economic data into actionable business insights.
11 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Finance, Master of Science - MS, Finance at The George Washington UniversityExchange Student, Finance, Exchange Student, Finance at Renmin University of ChinaBachelor's degree, E-commerce, Bachelor's degree, E-commerce at Guangxi University
Github Skills (24)
kubernetes10
docker10
python10
mysql10
kubeflow10
machine-learning10
dockers10
cicd10
go10
mlops10
tensorflow10
azure-devops10
jsonnet10
kubernetes-pods10
yaml10
Programming languages (9)
Github contributions (5)
kubeflow/katib
Dec 2018 - Jan 2020
Automated Machine Learning on Kubernetes
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:116 commits, 132 PRs, 1073 comments in 1 year 1 month
Contributions summary:Honggang primarily contributed to improving the study job controller functionality within the Katib project. Their work included fixing issues related to worker kinds and study job status, enhancing the stability of the system. Furthermore, they addressed an issue preventing StudyJobs from functioning correctly in non-Kubeflow namespaces. They also implemented code to delete obsolete data in the database.
xgboosthyperparametermachine-learninglightgbmautoml
kserve/kserve
Apr 2019 - Nov 2019
Standardized Serverless ML Inference Platform on Kubernetes
Role in this project:MLOps Engineer
MLOps EngineerContributions:48 commits, 30 PRs, 223 comments in 7 months
Contributions summary:Honggang primarily focused on the infrastructure and deployment aspects of the KServe platform. Their contributions included fixing test errors, setting up and configuring Prow for continuous integration, and modifying build scripts to include image creation. They also updated code related to virtual service configuration, demonstrating involvement in service mesh integration and overall deployment improvements for the ML inference platform. Furthermore, the user worked on removing deprecated features.
xgboostsklearnserverlessclient-goknative
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