Huigang Chen
Data Scientist
Los Angeles Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolHuigang Chen is a data scientist with six years of focused industry experience and a PhD in electrical engineering, now applying advanced causal inference and ML techniques at Google in the Los Angeles area. He has held data science and leadership roles at Facebook and Uber and led simulation and optimization teams earlier in his career, bringing a rare blend of managerial experience and hands-on modeling. His open-source contributions to uber/causalml—adding IV estimators, 2SLS, policy/DR/DRIV learners, and fixing variance/optimizer bugs—demonstrate deep expertise in uplift modeling and practical causal methods. Known for bridging rigorous econometric thinking with production ML, he often surfaces subtle implementation fixes (e.g., SciPy–TensorFlow dependency and Dragonnet Adam handling) that improve reliability. Colleagues rely on him to translate complex causal problems into reproducible code and clear examples that accelerate team adoption.
5 years of coding experience
15 years of employment as a software developer
no 2 middle school of east normal universityPhd Electrical Engineering, Phd Electrical Engineering at University of MarylandBS Automatic Control, BS Automatic Control at Shanghai Jiao Tong University
Github Skills (10)
scikit-learn10
pandas10
machine-learning10
python10
causal-inference10
scikit10
modeling9
tensorflow9
lifting9
lift9
Programming languages (2)
Github contributions (5)
uber/causalml
Jun 2020 - Dec 2021
Uplift modeling and causal inference with machine learning algorithms
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 25 commits, 7 PRs in 1 year 6 months
Contributions summary:Huigang's primary contribution involves adding and improving instrumental variable (IV) estimators within the causalml library. Their work includes implementing a 2SLS estimator with accompanying examples and updating dependencies, specifically addressing a SciPy requirement related to TensorFlow. Furthermore, the user added policy learner, DR learner, and DRIV learner implementations, including example notebooks and documentation enhancements for the DR and DRIV learners, expanding the capabilities of the causalml library. They also fixed a bug in DRIV learner variance calculation and in Dragonnet with the Adam learning rate parameter.
fairness-mldeep-learningmachine-learning-algorithmscausalinference
huigangchen/gsynth
Oct 2021 - Aug 2022
Generalized Synthetic Control Method
Contributions:5 pushes in 10 months
synthetic-control-methodmethodsynthetic
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