REU Site: The future of discovery: training students to build and apply open source machine learning models and tools

REU 网站：发现的未来：培训学生构建和应用开源机器学习模型和工具

• 批准号: 2050195
• 负责人: Volodymyr Kindratenko
• 金额: $ 40.5万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050195&HistoricalAwards=false
• 关键词: REU; Site; future; discovery; training

Machine learning is a powerful tool that has been successfully applied to a variety of problems that until recently were deemed too difficult or impossible for computers to solve. This REU Site project gives participating students experience in many aspects of machine learning, ranging from developing open source machine learning models and tools to applying them in the real world. The work carried out by the students will lead to research advances in the fields of these projects and the models and tools they develop will be open-source, leading to them being available to other fields where these models can be used to make additional advances. Machine learning is an emerging field with limitless opportunities to design innovative services and products that will enhance the lives of billions of people, help to address emerging challenges in climate, food, water, energy, transportation, and healthcare, and advance science and engineering discoveries in ways unimaginable today. The project contributes to the development of a highly specialized workforce trained to utilize advanced machine learning methods, and to contribute to open source software. Students from diverse backgrounds and computational/data-oriented disciplines are being trained to apply machine learning and to participate in research where these tools are at the center of scientific discovery, preparing them to apply machine learning methods in other fields and providing them with the foundation and motivation to pursue advanced graduate studies. This project serves NSF's mission by promoting the progress of science and advancing national health, prosperity and welfare. The goals of this project are to train undergraduate students, focusing on those from minority serving institutions, in machine learning and open source software, where they will then apply these skills to mentor-guided research projects. This is an on-site summer program at the University of Illinois that brings to campus 10 students per year and is based on matching their preferences and interests to those of a group of mentors, so that each student works with a pair of mentors, one from the project's research area and the other with expertise in machine learning. This program increases the students' knowledge of research and graduate school, and in many cases, stimulates their interest in continuing to graduate school, while in other cases, trains students with skills that enable them to seek data science and data analysis jobs in industry, increasing diversity in these graduate programs and in industry. By their presence in the program as continuing undergraduates, when the students return to their university, they will build a relationship between Illinois and that university, their faculty, and their peers that encourages future students to participate in the program and provides the basis for future joint research projects.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.

机器学习是一种强大的工具，已成功应用于直到最近被认为计算机难以解决或不可能解决的各种问题。该 REU 站点项目为参与的学生提供了机器学习许多方面的经验，从开发开源机器学习模型和工具到在现实世界中应用它们。学生们所做的工作将带来这些项目领域的研究进展，他们开发的模型和工具将是开源的，从而使它们可用于其他领域，在这些领域中，这些模型可用于取得额外的进展。机器学习是一个新兴领域，拥有无限的机会来设计创新服务和产品，这些服务和产品将改善数十亿人的生活，帮助解决气候、食品、水、能源、交通和医疗保健方面的新挑战，并推进科学和工程发现以今天难以想象的方式。该项目有助于培养高度专业化的劳动力队伍，接受培训以利用先进的机器学习方法，并为开源软件做出贡献。来自不同背景和计算/数据导向学科的学生正在接受培训，以应用机器学习并参与以这些工具为科学发现中心的研究，为他们在其他领域应用机器学习方法做好准备，并为他们提供基础和追求高级研究生学习的动力。该项目通过促进科学进步和促进国民健康、繁荣和福利来服务 NSF 的使命。 该项目的目标是培训本科生，重点关注来自少数族裔服务机构的本科生，学习机器学习和开源软件，然后他们将这些技能应用于导师指导的研究项目。这是伊利诺伊大学的一个现场暑期项目，每年吸引 10 名学生来到校园，该项目的基础是将他们的偏好和兴趣与一组导师的偏好和兴趣相匹配，以便每个学生与一对导师一起工作，其中一个是来自该项目的研究领域，另一位则拥有机器学习方面的专业知识。该计划增加了学生的研究和研究生知识，在许多情况下，激发了他们继续读研究生的兴趣，而在其他情况下，培训学生技能，使他们能够在工业界寻找数据科学和数据分析工作，这些研究生课程和行业的多样性不断增加。通过作为继续本科生参与该计划，当学生返回大学时，他们将在伊利诺伊州与该大学、教师和同龄人之间建立一种关系，鼓励未来的学生参与该计划，并为未来的学习奠定基础该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Novel Approaches Toward Scalable Composable Workflows in Hyper-Heterogeneous Computing Environments

• DOI: 10.1145/3624062.3626283
• 发表时间: 2023-11
• 期刊: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
• 作者: Jonathan Bader;Jim Belak;Matt Bement;Matthew Berry;Robert Carson;Daniela Cassol;Stephen Chan;John Coleman;Kastan Day;Alejandro Duque;Kjiersten Fagnan;Jeff Froula;S. Jha;Daniel S. Katz;Piotr Kica;Volodymyr V. Kindratenko;Edward Kirton;Ramani Kothadia;Daniel E. Laney;Fabian Lehmann;Ulf Leser;S. Lichołai;Maciej Malawski;Mario Melara;Elais Player Jackson;M. Rolchigo;Setareh Sarrafan;Seung-Jin Sul;Abdullah Syed;L. Thamsen;Mikhail Titov;M. Turilli;Silvina Caíno-Lores;Anirban Mandal

Spatial Analysis of Tumor Heterogeneity Using Machine Learning Techniques

使用机器学习技术对肿瘤异质性进行空间分析

• DOI: 10.1109/mass56207.2022.00123
• 发表时间: 2022
• 期刊: 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems (MASS
• 作者: Mitra, Chancharik;Yoo, Jin Young;Madak-Erdogan, Zeynep;Soliman, Aiman
• 通讯作者: Soliman, Aiman