Tiffany Lin
Machine Learning Engineer at HP
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTiffany Lin is a Machine Learning Engineer with nine years of practical experience building AI-driven products and automation, now based in Mountain View. Trained in both Computer Science and Cognitive Science at UC Berkeley, she specializes in generative AI, interpretability, and human-centered ML solutions that prioritize intuitive user experiences. As founder of Futron she validated market demand through interviews and surveys with property managers and shipped prototype chatbots and automations demonstrating strong prompt-engineering and product instincts. Her open-source contributions to the imodels package—adding robust discretizers and tests—reflect a focus on transparent, interpretable predictive modeling. Tiffany has production experience across cloud and frontend stacks at startups and enterprise settings (HP, Rely), with measurable impacts like halved identity-verification times and notable efficiency gains from data-process improvements. She combines entrepreneurial grit (a prior ecommerce venture that scaled from $100 to $200K) with rigorous ML engineering and user-centered research.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Arts - BA, Computer Science & Cognitive Science, Bachelor of Arts - BA, Computer Science & Cognitive Science at University of California, Berkeley
Chinese, English
Github Skills (10)
scikit10
pandas10
machine-learning10
interpretation10
random-forest10
python10
data-science10
random-forest-classifier10
scikit-learn10
data-analysis9
Programming languages (3)
Github contributions (5)
csinva/imodels
Jun 2021 - Jan 2023
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Role in this project:
Data Scientist
Data ScientistContributions:46 commits, 3 PRs, 64 pushes in 1 year 7 months
Contributions summary:Tiffany's contributions primarily center on developing and implementing a `BasicDiscretizer` class and an `RFDiscretizer` class within the `imodels` package. These classes provide functionality to discretize numeric data into bins using methods like `KBinsDiscretizer` and random forest splits, with options for encoding (one-hot or ordinal) and binning strategies. The user implemented various tests for the discretizer classes. These changes enhance the interpretability and usability of the `imodels` package.
pythonxaisklearnsklearn-compatibleinterpretable
Yu-Group/simChef
Jul 2021 - Jan 2023
An R package to facilitate PCS simulation studies.
Contributions:1 release, 36 reviews, 319 commits in 1 year 5 months
r-packagesimulationmissing-datameta-analysispcs
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