Ian Tenney
Staff Research Scientist, Manager at Google DeepMind
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolIan Tenney is a Staff Research Scientist and manager specializing in interpretability and explainable AI, currently leading work at Google DeepMind after a decade-plus career at Google Research and PAIR. He blends research rigor with production-minded engineering, contributing to tools like the widely used jiant NLP toolkit (infrastructure and cloud setup) and the PAIR "lit" interpretability UI (front-end enhancements). His background spans deep learning for NLP, large-scale ML systems, and teaching—he co-created and taught an NLP course at UC Berkeley and holds an MS in Computer Science and a BS in Physics from Stanford. Known for improving both model understanding and developer workflows, he brings a rare mix of UI finesse, backend/devops chops, and experimental physics-trained curiosity to complex AI problems.
11 years of coding experience
13 years of employment as a software developer
Master of Science (MS) Computer Science, Master of Science (MS) Computer Science at Stanford University
Github Skills (17)
javascript10
python10
scripting10
typescript10
script10
front-end-development10
typescript-types10
sh10
typescripts10
shell10
devops10
visualization9
visualizations9
nlp9
html8
Programming languages (5)
Github contributions (5)
nyu-mll/jiant
Jun 2018 - Jul 2020
jiant is an nlp toolkit
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:373 commits, 109 PRs, 249 pushes in 2 years 1 month
Contributions summary:Ian's contributions focused on improving the project's infrastructure and build processes. They made changes to the project's source code, updating paths and adding files to improve the pre-processing and execution of the NLP toolkit. Additionally, they worked on the project's setup scripts by fixing paths and configuring the environment for cloud execution.
nlptransformersmultitask-learningsentence-representationbert
PAIR-code/lit
Sep 2020 - Jan 2023
The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.
Role in this project:Front-end Developer
Front-end DeveloperContributions:1 release, 32 reviews, 214 commits in 2 years 5 months
Contributions summary:Ian primarily focused on enhancing the user interface and functionality of the Learning Interpretability Tool. Their contributions involved matching header colors in the span graph visualization and fine-tuning numerical edge labels to improve readability. The user also addressed display issues in the datapoint editor and data table, including adding non-breaking space characters to the string, to maintain the appearance of strings in the right manner. Additionally, they implemented adjustments to display and improved UI controls to improve user experience.
ml-modelspythoninterpretabilitydata-sciencedeep-learning
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