James Wexler
Staff Software Engineer at Google
Greater Boston United States
Summary
🤩
Rockstar🎓
Top SchoolJames Wexler is a Staff Software Engineer with a decade of experience building visualization and interpretability tools for machine learning, currently at Google’s People+AI Research (PAIR) initiative in Greater Boston. He combines front-end polish and full-stack engineering with ML expertise, contributing to high-profile open-source projects like TensorBoard, tfjs, and LIT to make model behavior and datasets more understandable. His work spans prototyping teachable demos (TopK image classifier, js-dos integrations) to hardening production visualization logic and unit testing for interpretability projects such as TCAV. Prior roles at Amazon, Microsoft, and Raytheon reflect deep systems and performance experience, including leading 3D controls and high-availability radar software. Colleagues rely on him for bridging research and product: shipping interactive UIs, cross-browser webcam performance fixes, and nuanced graph-visualization bug fixes that improve ML debugging at scale.
10 years of coding experience
18 years of employment as a software developer
BS, Computer Science, BS, Computer Science at University of RochesterMS, Computer Science, MS, Computer Science at Northeastern University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (35)
data-visualizations10
unit-testing10
webgl10
javascript10
interpretation10
python10
mobx10
testing10
css10
tensorflowjs10
machine-learning10
typescript10
data-visualisation10
tensorflow10
front-end-development10
Programming languages (9)
Github contributions (5)
PAIR-code/lit
Jul 2020 - Nov 2022
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 & ML Engineer
Front-end Developer & ML EngineerContributions:6 releases, 32 reviews, 230 commits in 2 years 3 months
Contributions summary:James's commits primarily focus on front-end development, including the addition of image upload functionality, restructuring of the data table for improved display, and implementing new components like the threshold slider. They are also actively involved in integrating and visualizing machine learning results, specifically adding a multilabel results module and incorporating partial dependence plot visuals within the LIT environment. The contributions include addressing user interface aspects as well as features related to machine learning models.
mlmodelmachine-learningnatural-language-processingvisualization
PAIR-code/facets
Jul 2017 - Jan 2023
Visualizations for machine learning datasets
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:4 releases, 4 reviews, 175 commits in 5 years 7 months
Contributions summary:James primarily contributed to the frontend of the project, making changes to the demonstration notebooks by converting Python-based stats generators into classes. They also fixed demo notebooks by removing unnecessary code and refactored styling and visual elements in the facets overview chart. In addition, they fixed polymer paths for a more stable build environment.
machine-learningvisualizationsdata-visualization
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