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

机器学习是一种强大的工具，已经成功地应用于各种直到最近还被认为太难或不可能由计算机解决的问题。这个REU站点项目为参与的学生提供了机器学习的许多方面的经验，从开发开放源代码的机器学习模型和工具到将它们应用于现实世界。学生开展的工作将导致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，从而使它们可以用于其他领域，这些模型可以用来取得更多进展。机器学习是一个新兴领域，拥有无限的机会来设计创新的服务和产品，这些服务和产品将改善数十亿人的生活，帮助应对气候、食品、水、能源、交通和医疗保健方面的新挑战，并以当今无法想象的方式推进科学和工程发现。该项目有助于发展一支高度专业化的劳动力队伍，经过培训，利用先进的机器学习方法，并为开放源码软件做出贡献。来自不同背景和以计算/数据为导向的学科的学生正在接受培训，以应用机器学习并参与这些工具处于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，并为他们提供继续深造的基础和动力。该项目服务于国家科学基金会的使命，促进科学进步，促进国民健康、繁荣和福利。这个项目的目标是培训本科生，重点是那些来自少数族裔服务机构的学生，在机器学习和开源软件方面，然后他们将在导师指导的研究项目中应用这些技能。这是伊利诺伊大学的一个现场暑期项目，每年将10名学生带到校园里，基于将他们的偏好和兴趣与一组导师的偏好和兴趣相匹配，以便每个学生都与一对导师合作，一位来自该项目的研究领域，另一位具有机器学习方面的专业知识。该项目增加了学生对研究和研究生院的知识，在许多情况下，激发了他们继续研究生院学习的兴趣，而在其他情况下，培训学生的技能使他们能够在行业中寻找数据科学和数据分析工作，增加了这些研究生项目和行业的多样性。通过他们作为继续本科生参加该项目，当学生返回他们的大学时，他们将在伊利诺伊州与该大学、他们的教职员工和他们的同龄人之间建立一种关系，鼓励未来的学生参与该项目，并为未来的联合研究项目提供基础。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.