Terence Parr
Senior Staff Software Engineer at ANTLR
San Francisco, California, United States
Summary
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Rockstar🎓
Top School
Top expert inCross-Platform Development and Programming Languages
Terence Parr is a Senior Staff Software Engineer and long-time creator of developer tooling, best known as the author and project lead of the widely used ANTLR parser generator. With 30+ years building libraries, compilers, and online communities, he blends deep programming-language expertise with practical ML tooling—recently leading efforts at Google to make LLMs accessible to non-ML programmers and to harden models against adversarial prompts. He has an academic pedigree as a former professor who co-founded USF’s MS in Data Science and continues to publish books and papers while prioritizing shipping software. His open-source work spans language grammars, template engines, and model-interpretability libraries (e.g., dtreeviz and random-forest-importances), reflecting a rare combination of compiler craftsmanship and data-science pragmatism. Based in San Francisco, he focuses on developer productivity and explainable ML, often contributing subtle refactors and usability improvements that disproportionately improve downstream adoption.
21 years of coding experience
32 years of employment as a software developer
Ph.D. Computer Engineering, Ph.D. Computer Engineering at Purdue University
Stackoverflow
5,932reputation
217kreached
161answers
2questions
antlr
top-1%
grammar
top-5%
java
top-5%
freemarker
top-5%
parsing
top-5%
Github Skills (41)
data-visualizations10
apim10
python10
data-science10
feature-selection10
scikit10
pandas10
machine-learning10
data-visualisation10
template-engine10
java10
grammar10
javas10
grammars10
code-generation10
Programming languages (14)
Github contributions (5)
parrt/random-forest-importances
Mar 2018 - Jan 2021
Code to compute permutation and drop-column importances in Python scikit-learn models
Role in this project:
Data Scientist
Data ScientistContributions:5 releases, 2 reviews, 246 commits in 2 years 10 months
Contributions summary:Terence appears to be working on a project related to machine learning with random forests, based on the code additions within the specified repository. The commits demonstrate the implementation and exploration of feature importance techniques, including the calculation of permutation importances and their visualization. The user's focus includes applying these methods to real-world data sets, such as the rent dataset, to analyze and interpret feature relevance within the models, suggesting a core focus on data analysis and model interpretability. Furthermore, the user has implemented methods for dealing with multicollinear features.
pythondata-sciencemachine-learningscikit-learnscikit-learn-python
parrt/dtreeviz
Aug 2018 - Jan 2023
A python library for decision tree visualization and model interpretation.
Role in this project:Data Scientist
Data ScientistContributions:37 releases, 87 reviews, 320 commits in 4 years 6 months
Contributions summary:Terence's commits focused on refactoring and improving the tree visualization library. They modified code related to tree visualization, including the addition of a scale parameter. These contributions suggest a focus on improving the clarity and effectiveness of the visualizations. The changes were performed in the context of a library designed for decision tree visualization, indicating the user's involvement in enhancing model interpretability.
xgboostdecision-treemodel-interpretationpythonrandom-forest
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