Gerald Woo
Senior Research Scientist at Datadog
United States
Summary
🤩
Rockstar🎓
Top SchoolGerald Woo is a Senior Research Scientist with nine years of experience applying AI/ML to large-scale, real-world problems across industry leaders including Meta, Salesforce, and Datadog. He specializes in time series forecasting and helped develop one of the first foundation models for time series, contributing substantive code and fixes to the popular GluonTS library. Gerald blends academic rigor—a PhD in Computer Science—with hands-on production work on feed relevance and observability systems, moving models from research into high-throughput services. His background spans fraud detection, robotic process automation, and defenses against ML backdoors, reflecting a breadth beyond core modeling. Known for pragmatic engineering, he exposes model configurability and robustness improvements in open-source projects to make advanced methods more usable in practice. Based in the United States, he continues to push on scalable AI research while keeping a strong foothold in time series innovation.
9 years of coding experience
2 years of employment as a software developer
Bachelor of Engineering - BE, Information Systems Technology and Design, Summa Cum Laude, Bachelor of Engineering - BE, Information Systems Technology and Design, Summa Cum Laude at Singapore University of Technology and Design (SUTD)Doctor of Philosophy - PhD, Computer Science, Summa Cum Laude, Doctor of Philosophy - PhD, Computer Science, Summa Cum Laude at Singapore Management UniversitySt. Andrew's Junior College
Stackoverflow
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Github Skills (13)
neural-network10
forecasting10
pytorch10
deeplearning-ai10
time-series10
machine-learning10
forecast10
deep-learning10
python10
artificial-neural-networks10
data-science9
scipy8
mxnet4
Programming languages (5)
Github contributions (5)
awslabs/gluonts
Aug 2022 - May 2023
Probabilistic time series modeling in Python
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 6 PRs, 17 comments in 9 months
Contributions summary:Gerald made multiple contributions related to the implementation and improvement of time series forecasting models in the GluonTS library. They exposed parameters in the Torch TFT estimator class, allowing for more flexible model configuration. Additionally, they fixed issues related to validation data and NaNs, ensuring the robustness and accuracy of model training and evaluation, while also modifying the StudentT distribution.
forecastingpythontime-series-analysistimeseries-forecastingaws
gorold/dcgan-cifar10
Jan 2019 - Jan 2019
Contributions:7 commits, 6 pushes, 1 branch in 2 days
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