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Lezhi Li

Lezhi Li
3 BLOG ARTICLES 1 RESEARCH PAPERS
Lezhi Li is a software engineer on Uber's Machine Learning Platform team.

Engineering Blog Articles

Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning

First introduced by Uber Engineering in January 2019, Manifold is a visual debugging tool that enables users to quickly identify performance issues in machine learning models.
San Francisco map showing average, clustered traffic speeds

Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp

As part of Uber Visualization's all-team hackathon, we built SpeedsUp, a project using machine learning to process average speeds across a city, cluster the results, and overlay them on a street map.

Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Uber built Manifold, a model-agnostic visualization tool for ML performance diagnosis and model debugging, to facilitate a more informed and actionable model iteration process.

Research Papers

Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

J. Zhang, Y. Wang, P. Molino, L. Li, D. Ebert
Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. [...] [PDF]
IEEE Visualization (IEEE VIS), 2018

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