Wenling Jiang
Product Manager at 腾讯科技
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolWenling Jiang is a product manager with eight years of experience blending statistical rigor and product strategy at leading Chinese tech firms including Tencent and Baidu. With an M.S. in Statistics from the University of Chicago and a background in actuarial science, she excels at turning complex spatio-temporal and behavioral data into actionable product insights using advanced ML techniques. Her open-source contributions to LibCity—improving trajectory prediction models like DeepMove and integrating diverse algorithms—underscore a hands-on mastery of urban data mining and experiment design. Colleagues describe her as an analytical, fast-learning team player who pairs creative problem solving with practical model evaluation. Unusually for a product manager, she has deep experience in negative-sample handling and dataset integration, which helps bridge research-grade models and production needs.
8 years of coding experience
4 years of employment as a software developer
Master of Science (M.S.), Statistics, Master of Science (M.S.), Statistics at University of ChicagoBachelor, Actuarial Science with Finance Option and Statistics, Bachelor, Actuarial Science with Finance Option and Statistics at University of Waterloo
English, Chinese
Github Skills (14)
data-preprocessing10
model-building10
pytorch10
machine-learning10
deep-learning10
py10
python10
evaluation10
modeling10
data-science10
metric10
model-driven-development10
model-driven10
pythorch10
Programming languages (3)
Github contributions (5)
LibCity/Bigscity-LibCity
Dec 2020 - Jan 2023
LibCity: An Open Library for Urban Spatial-temporal Data Mining
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:1 release, 61 reviews, 89 commits in 2 years
Contributions summary:Wenling primarily contributed to the implementation and refinement of trajectory location prediction models within the LibCity framework. Their work involved updating and refactoring the TrajLocPredEvaluator and the core DeepMove model, including loss calculation and prediction functions. The user also integrated new models like FPMC, LSTPM, STRNN, and CARA, indicating a focus on expanding the library's capabilities with diverse trajectory prediction algorithms. Furthermore, they addressed issues related to the handling of negative samples and dataset integration, as well as experiment configurations.
estimated-time-of-arrivaletarepresentation-learningdata-miningmap-matching
LibCity/Bigscity-LibCity-PaperList
May 2021 - Nov 2021
Paper list in traffic prediction field
Contributions:14 commits, 11 pushes in 5 months
deep-learningestimated-time-of-arrivalgraph-neural-networkslibcitymap-matching
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