REU Site: Drivers for Machine Learning and Artificial Intelligence Practices (MAPs)

REU 网站：机器学习和人工智能实践 (MAP) 的驱动因素

• 批准号: 2244580
• 负责人: Angela Green
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244580&HistoricalAwards=false
• 关键词: REU; Site; Drivers; Machine; Learning

Machine learning and artificial intelligence have demonstrated potential to vastly alter the efficiencies with which we operate across a multitude of applications. Biological systems that stand to benefit from advanced techniques in machine learning and artificial intelligence include all areas of digital agriculture that impact the natural earth, plants, and animals. The Research Experience for Undergraduates (REU) Site projects represent research that addresses some of the most pressing challenges to the sustainability of our world, bringing together some of the most difficult challenges at the intersection of human behavior, natural systems, and cutting-edge technology. The project serves to offer highly specialized workforce training to students with backgrounds in either computation or biological systems and offers training to work across disciplines. Equipping a generation of students to work across disciplines reveals the opportunity for addressing grand challenges with robust problem solving.Machine learning must be able to recognize and respond to complexities of the system, and biological applications bring a complex set of challenges. This REU program lies in the cross-disciplinary requirements to understand and create new advanced machine learning techniques in complex biological systems. The work carried out by students will focus on cutting edge techniques and emerging challenges. The REU program will bring three groups of 10 students together for 10 weeks of research in machine learning in biological system applications. The students will be recruited from different academic and cultural backgrounds, with REU program goals to improve their research skills; increase opportunities for success for underserved students; and prepare students for graduate school and industry opportunities in cross-disciplinary teams. We expect that all Drive for MAPs REU students will increase their competency and fluency with respect to machine learning and across complex biological applications. The site also strives to build relationships with the student’s home universities to continue to expand collaborative efforts for 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.

机器学习和人工智能已经表现出了极大地改变我们在众多应用程序中运行的效率的潜力。将受益于机器学习和人工智能中先进技术的生物系统包括影响自然地球，植物和动物的所有数字农业领域。本科生（REU）现场项目的研究经验代表了解决世界可持续性最紧迫的挑战的研究，在人类行为，自然系统和尖端技术的交汇处汇集了一些最困难的挑战。该项目旨在为具有计算或生物系统背景的学生提供高度专业的劳动力培训，并提供跨学科工作的培训。装备一代学生跨学科工作，可以通过解决强大的问题解决方面解决巨大的挑战的机会。机器学习必须能够识别并应对系统的复杂性，并且生物应用程序带来了一系列复杂的挑战。此REU计划在于跨学科的要求，以理解和创建复杂的生物学系统中的新高级机器学习技术。学生进行的工作将专注于最先进的技术和新兴的挑战。 REU计划将使三组10名学生聚集在一起，以进行10周的生物系统应用机器学习研究。将从不同的学术和文化背景中招募学生，并具有REU计划的目标，以提高他们的研究技能；增加服务不足的学生的成功机会；并为学生准备跨学科团队的研究生和行业机会。我们希望所有驱动力的REU学生都将在机器学习和复杂的生物学应用方面提高他们的能力和流畅性。该网站还努力与学生的家乡建立关系，以继续扩大研究的协作努力。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的支持。