CAREER: Resource Efficient Systems for Machine Learning on Structured Data

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Many scientific and enterprise datasets include relationships among data items, which can be represented as graphs. Applying machine learning methods on such graph datasets can yield benefits across several domains, including social networks, drug discovery, and search engines. However, existing software for applying machine learning methods on large graph datasets is slow, complex, and expensive. This NSF CAREER proposal aims to address these challenges by developing software that will make it faster, easier, and less expensive to analyze large graph datasets. The proposed research includes three thrusts that focus on different stages of machine learning workflows. The first thrust aims to develop software that will make it faster and easier to train machine learning models on large graphs using many machines. The second thrust focuses on how to efficiently handle scenarios where graphs are updated with additional data. The third thrust considers how to make prediction less expensive when using machine learning models trained on large graph datasets. The broader impacts of the proposed research include improved analysis capabilities for data scientists working in many areas. Furthermore, all software developed as a part of this project will be made freely available to the wider community and will include documentation and tutorials to help users from computer science and other academic disciplines to get started. Additionally, the proposal plans to develop a new undergraduate course that teaches students how to use software frameworks to process large datasets. The assignments in the course will use software tools developed as a part of this project. The project also includes plans to broaden participation in computer science by organizing a yearly workshop that promotes research opportunities for undergraduate students from underrepresented groups, as well as discussion sessions that can help students who are getting started with research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.