Huibin Shen
Sr. Applied Scientist at Amazon Web Services (AWS)
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolHuibin Shen is a senior applied scientist with 11 years of experience specializing in machine learning, probabilistic time series, and production-ready research at Amazon and AWS in Berlin. He holds a Ph.D. in machine learning and computational biology from Aalto and an M.Sc. from the University of Helsinki, bringing strong academic rigor to applied ML problems. At AWS he focuses on scalable forecasting and has contributed to GluonTS, improving transformation checks and GPU forecasting reliability in a widely used probabilistic time series library. His background blends deep research, software engineering, and data science, enabling him to move models from prototype to robust deployment. Known for attention to quality in transformation pipelines, he often surfaces subtle correctness issues that improve end-to-end model reliability. Fluent in both research-driven experimentation and production constraints, he thrives on turning complex probabilistic models into reliable services.
11 years of coding experience
Bachelor's degree, Computer Software Engineering, Bachelor's degree, Computer Software Engineering at East China Normal UniversityDoctor of Philosophy (Ph.D.), Machine learning and Computational Biology, Doctor of Philosophy (Ph.D.), Machine learning and Computational Biology at Aalto UniversityMaster's degree, Machine Learning, Master's degree, Machine Learning at University of Helsinki
Chinese, English
Github Skills (15)
forecasting10
mxnet10
machine-learning10
deeplearning-ai10
time-series10
forecast10
deep-learning10
python10
data-science9
testing9
pytorch9
pandas8
data-structures8
numpy8
data-structure8
Programming languages (5)
Github contributions (5)
awslabs/gluonts
Jan 2022 - Jan 2022
Probabilistic time series modeling in Python
Role in this project:
Data Scientist
Data ScientistContributions:57 reviews, 1 commit, 6 PRs in 1 day
Contributions summary:Huibin contributed to the GluonTS repository by improving the quality checks for time series transformations. Their work included adding and testing equality checks for various transformations, ensuring the proper functioning of the time series modeling pipeline. This involved modifying files related to testing and core transformation logic, demonstrating a focus on ensuring the reliability of transformation components within the GluonTS framework. Additionally, the user worked on model functionality related to forecasting distributions on GPUs.
forecastingpythontime-series-analysistimeseries-forecastingaws
huibinshen/fingerid
Jul 2014 - Feb 2018
Contributions:28 commits, 24 pushes, 11 comments in 3 years 8 months
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