Peiyi Zhang
Research Data Scientist
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolPeiyi Zhang is a Research Data Scientist in the San Francisco Bay Area with four years of experience applying probabilistic and generative modeling to complex dynamic systems. At Facebook she focuses on scalable time-series and anomaly-detection solutions, contributing notable enhancements to the widely used Kats toolkit (improving StatSigDetector, seasonality handling, and large-dataset strategies). She brings a strong academic foundation—PhD in Statistics and a joint CS/Statistics master’s from Purdue, plus a 4.0 Applied Statistics master’s from Cornell—and a track record as a researcher, instructor, and statistical consultant. Known for bridging rigorous uncertainty quantification with production-ready code, she pairs deep theoretical expertise with pragmatic engineering to make advanced models robust and performant in real-world settings.
4 years of coding experience
2 years of employment as a software developer
Exchange program, Exchange program at University of TorontoMaster’s Degree, Applied Statistics, 4.0/4.0, Master’s Degree, Applied Statistics, 4.0/4.0 at Cornell UniversityMaster's degree, Joint Computer Science and Statistics, 3.97/4.0, Master's degree, Joint Computer Science and Statistics, 3.97/4.0 at Purdue UniversityBachelor’s Degree, Statistics, Bachelor’s Degree, Statistics at Zhejiang University
English, Chinese
Github Skills (12)
scikit-learn10
data-analysis10
pandas10
time-series10
statistical-models10
anomaly-detection10
python10
scikit10
testing9
machine-learning9
forecast8
forecasting8
Programming languages (1)
Github contributions (1)
facebookresearch/Kats
Nov 2021 - Oct 2022
Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
Role in this project:
Data Scientist
Data ScientistContributions:1 review, 30 commits, 2 PRs in 11 months
Contributions summary:Peiyi primarily contributes to the `kats` repository, a time series analysis toolkit, by making improvements to anomaly detection capabilities. They have focused on enhancing the StatSigDetector, adding seasonality handling, and implementing strategies for handling large datasets. Their work includes modifying the core detection logic, integrating interpolation techniques, and refactoring code to optimize performance. The user also makes test-related changes to increase the robustness of the tests.
forecastingstatisticspythontime-series-analysisunderstanding
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