Towards Automated Understanding of Scientific Software

Advisor: Daniel Garijo (Viterbi School of Engineering)
Students: Haripriya Dharmala , Jiaying Wang , Vedant Diwanji

Description

Data science projects require knowledge of software that changes rapidly. As a result, scientists spend hours reading long documentations and manuals instead of advancing their scientific fields. In this project, we aim to automatically extract relevant aspects of scientific software (e.g., what does it do, how to install it, how to operate with it or how to cite it) from documentation and code using machine learning techniques. The students will build on an existing baseline of classifiers and try to improve the existing results.

Awards

  • Best Data Science Open and Sharing Practices

  • Best Project Presentation

  • Highlighted Project

Students

Advisors

Skills Required by the team

  • Python
  • Knowledge Graphs
  • Machine Learning
  • Sklearn

Final presentation resources

Spring 2020 Featured Best Data Science Open and Sharing Practices Best Project Presentation Highlighted Project Technology Science