Web-based framework for rich annotation interfaces
David Bamman, Professor
Information, School of
Applications for Spring 2025 are closed for this project.
In machine learning, manual data annotation is often required for training or evaluation. While there are a number of annotation tools available, most are focused on specific tasks or annotation formats, and often do not work well with multiple modalities (e.g., text and video). In this project, you will contribute to a Python library that abstracts away the annotation management logic (users, data, etc.) and makes creating novel annotation interfaces much easier.
Role: Tasks in this role include front-end and back-end web programming. You will contribute to 1) improving the developer experience of the framework, 2) using the framework to build out components and interfaces, and 3) writing tests and improving the code-base. You will be expected to attend weekly meetings to discuss progress and roadblocks.
Qualifications: Python programming experience (Data 100 or similar), web development knowledge (JS/HTML/CSS), and a familiarity with git is required. Experience with using Flask or Svelte would be useful, but not required.
Day-to-day supervisor for this project: Naitian Zhou, Graduate Student
Hours: 9-11 hrs
Off-Campus Research Site: Online
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