Jeong-yoon Lee
Sr. Manager, Applied Science at University of Chicago
Los Angeles, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJeong-yoon Lee is a senior applied science leader in Los Angeles with over a decade of hands-on experience building and scaling causal inference and recommendation systems at companies including Uber, Netflix, and Microsoft. He combines PhD-level research rigor with product-focused delivery—leading teams at Uber while teaching as a lecturer at the University of Chicago. An active open-source contributor, he has made substantive algorithmic and back-end contributions to the widely used causalml library and maintains Kaggle-focused tooling that improves data I/O and evaluation metrics like quadratic weighted kappa. A practical problem-solver and repeat founder/board member, he brings entrepreneurial instincts to large-scale machine learning problems and a knack for translating research into production. Off-hours he’s a devoted father of five, reflecting strong personal discipline and time-management skills.
11 years of coding experience
12 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Southern CaliforniaBachelor of Science - BS Electrical Engineering, Bachelor of Science - BS Electrical Engineering at Seoul National University
English, Korean
Stackoverflow
1,429reputation
459kreached
3answers
0questions
pandas
top-5%
python
top-5%
Github Skills (18)
scipy10
kaggle10
python10
scikit10
pandas10
machine-learning10
causal-inference10
numpy10
lift10
cython10
data-processing10
scikit-learn10
lifting10
modeling10
hdf9
Programming languages (5)
Github contributions (5)
uber/causalml
Jul 2019 - Sep 2022
Uplift modeling and causal inference with machine learning algorithms
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:12 releases, 163 reviews, 186 commits in 3 years 2 months
Contributions summary:Jeong-yoon appears to be modifying and expanding upon existing machine learning code for causal inference and uplift modeling. The commits demonstrate a focus on implementing and updating causal tree-based algorithms, including the CausalTreeRegressor. The user's contributions encompass modifications to core algorithms and file structure, indicating a role in developing and refining core machine learning functionality within the CausalML library.
fairness-mldeep-learningmachine-learning-algorithmscausalinference
jeongyoonlee/Kaggler
Jan 2015 - Mar 2022
Code for Kaggle Data Science Competitions
Role in this project:Data Scientist
Data ScientistContributions:20 releases, 1 review, 157 commits in 7 years 2 months
Contributions summary:Jeong-yoon primarily contributed to data I/O functionality, adding support for saving and loading data in HDF5 format. They refactored existing data loading and saving functions to handle both sparse and dense matrices, improving the library's flexibility. Additionally, the user addressed errors in the data processing steps, corrected import errors, and added utility functions, indicating a focus on improving the data handling and processing capabilities within the project, which aligns with the repository's focus on Kaggle data science competitions. Moreover, the addition of the `quadratic_weighted_kappa` metric suggests a focus on model evaluation for competitions where this metric is common.
kagglepythonsciencedata-sciencemachine-learning
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