CAREER: Resource Efficient Systems for Machine Learning on Structured Data

职业：结构化数据机器学习的资源高效系统

• 批准号: 2237306
• 负责人: Shivaram Venkataraman
• 金额: $ 67.61万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237306&HistoricalAwards=false
• 关键词: CAREER; Resource; Efficient; Systems; Machine

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.

许多科学和企业数据集都包含数据项之间的关系，这些关系可以表示为图。在此类图数据集上应用机器学习方法可以在包括社交网络，药物发现和搜索引擎在内的几个领域产生好处。但是，在大图数据集上应用机器学习方法的现有软件是缓慢，复杂且昂贵的。 NSF职业建议旨在通过开发软件来解决这些挑战，从而使其更快，更容易，分析大型图数据集的软件。拟议的研究包括三个推力，专注于机器学习工作流的不同阶段。第一个推力旨在开发软件，该软件将使使用许多机器在大图上训练机器学习模型更快，更容易。第二个推力重点是如何有效处理图形更新的方案，并使用其他数据更新。第三个推力考虑了在使用大型图形数据集训练的机器学习模型时如何使预测更便宜。拟议的研究的更广泛影响包括改进在许多领域工作的数据科学家的分析能力。此外，作为该项目的一部分开发的所有软件将免费提供给更广泛的社区，并将包括文档和教程，以帮助来自计算机科学和其他学术学科的用户开始。此外，该提案计划开发一门新的本科课程，该课程教会学生如何使用软件框架处理大型数据集。课程中的作业将使用开发的软件工具作为该项目的一部分。 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.