Chenghuang Shen
Director Alibaba Cloud Intelligence at 阿里巴巴
Hangzhou City, Zhejiang, China
Summary
👤
Senior🎓
Top SchoolChenghuang Shen is a seasoned cloud operations and support leader with over 15 years driving global programs and cross-region teams at Alibaba and Microsoft, currently directing Alibaba Cloud Intelligence across Asia Pacific and Greater China. He combines deep operational command—running global online services, contingency and emergency command systems—with business development and strategic initiative leadership for O365 support. Known for building high-performing technical and customer-facing teams, he has a track record of automating monitoring, incident management and delivering large-scale cost and service improvements. Chenghuang also contributes to open-source ML tooling, integrating NER models into ModelScope, reflecting hands-on engineering fluency alongside executive responsibility. Trained in applied mathematics at Fudan University, he brings disciplined quantitative thinking to complex, cross-boundary cloud service challenges.
11 years of coding experience
16 years of employment as a software developer
Bachelor's degree, Applied Mathematics, Bachelor's degree, Applied Mathematics at Fudan University
Chinese, Chinese
Github Skills (14)
model-building10
pytorch10
machine-learning10
nlp10
deep-learning10
integrate10
python10
integrations10
data-integration10
modeling10
model-driven10
model-driven-development10
computer-vision3
tensorflow3
Programming languages (3)
Github contributions (5)
modelscope/modelscope
Jul 2022 - Mar 2023
ModelScope: bring the notion of Model-as-a-Service to life.
Role in this project:
ML Engineer
ML EngineerContributions:17 commits, 1 PR in 7 months
Contributions summary:Chenghuang primarily contributed to the implementation and integration of Named Entity Recognition (NER) models within the ModelScope framework. Their work includes adding a new model named nncrf, incorporating transformer-crf and lstm-crf architectures, and integrating NER models. This involved adding NER metainfo and pipeline components, updating default models, and fixing tokenizer-related bugs. Furthermore, the user added unit tests for NER models, enhanced the pipeline by allowing inference on longer inputs and returning entity probabilities.
nlppytorchmulti-modalpythonscience
modelscope/AdaSeq
Sep 2022 - Feb 2023
AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
Contributions:5 reviews, 161 commits, 4 PRs in 5 months
sequencenatural-language-understandingdevelopingbertword-embeddings
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.