Maggie Hei
Data Scientist
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolMaggie Hei is a data scientist with nine years' experience applying machine learning and causal inference across industry and research, currently working on causal methods at ByteDance after adapting ML for causal problems at Microsoft Research. She has strong production analytics chops from building cloud-based hardware monitoring and predictive pipelines at HP using Spark and Databricks, and a master’s in statistics from Rice. Maggie contributes to the open-source EconML project, adding double machine learning examples, bootstrapped CIs, and evaluation tooling that help bridge econometrics and modern ML. Comfortable moving between research and production, she uniquely blends rigorous causal estimation with pragmatic data engineering to deliver actionable insights at scale.
9 years of coding experience
5 years of employment as a software developer
High School, High School at The Experimental High School Attached to Beijing Normal UniversityBachelor's degree, Information and computing science, 3.83/4.00, Bachelor's degree, Information and computing science, 3.83/4.00 at Beijing Jiaotong UniversityMaster's degree, Statistics, 3.70/4.00, Master's degree, Statistics, 3.70/4.00 at Rice University
English, Chinese
Github Skills (9)
causal10
machine-learning10
causality10
python10
causal-inference10
jupyter-notebook9
econometrics8
scikit7
scikit-learn7
Programming languages (2)
Github contributions (5)
py-why/EconML
Mar 2019 - Aug 2021
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Role in this project:
Data Scientist
Data ScientistContributions:170 reviews, 84 commits, 55 PRs in 2 years 5 months
Contributions summary:Maggie contributed to the project by fixing typos in the documentation and adding new double machine learning (DML) examples, including a notebook demonstrating DML usage with synthetic and observational data. They also modified the notebooks with updated plots and bootstrap confidence intervals. The user implemented and integrated a scoring function into the DML examples and tests, which enhanced the evaluation capabilities within the project.
decision-makingfairness-mlpythonoutcomeproblems
heimengqi/minthhh
Apr 2017 - Mar 2019
Contributions:1 push, 1 branch in 1 year 10 months
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