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证明有潜力极大地改变我们在众多应用程序中运行的效率。受益于机器学习和人工智能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）网站项目的研究经验，解决了一些最紧迫的挑战，我们的世界的可持续发展，汇集了一些最困难的挑战，在人类行为，自然系统和尖端技术的交叉点。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化的劳动力培训，并提供跨学科工作的培训。让一代学生能够跨学科工作，揭示了通过强大的问题解决方案应对重大挑战的机会。机器学习必须能够识别和应对系统的复杂性，而生物应用带来了一系列复杂的挑战。这个REU计划在于跨学科的要求，以了解和创造新的先进的机器学习技术在复杂的生物系统。学生开展的工作将侧重于尖端技术和新兴挑战。REU计划将把三组10名学生聚集在一起，进行为期10周的机器学习在生物系统应用中的研究。学生将从不同的学术和文化背景中招募，REU计划的目标是提高他们的研究技能;为服务不足的学生增加成功的机会;并为跨学科团队的研究生院和行业机会做好准备。我们希望所有Drive for MAPs REU学生都能提高他们在机器学习和复杂生物应用方面的能力和流畅性。该网站还努力与学生的家乡大学建立关系，继续扩大研究合作努力。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。