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

机器学习和人工智能已经显示出极大地改变我们在众多应用程序中操作效率的潜力。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、植物和动物的数字农业的所有领域。本科生研究体验（REU）网站项目代表的研究解决了我们世界可持续性面临的一些最紧迫的挑战，在人类行为、自然系统和尖端技术的交叉点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化的劳动力培训，并提供跨学科工作的培训。让一代学生具备跨学科的工作能力，揭示了用强大的问题解决能力应对重大挑战的机会。机器学习必须能够识别和响应系统的复杂性，而生物应用带来了一系列复杂的挑战。这个REU项目是跨学科的要求，以理解和创造复杂生物系统中新的先进机器学习技术。学生开展的工作将侧重于前沿技术和新兴挑战。REU计划将把三组10名学生聚集在一起，进行为期10周的生物系统应用中的机器学习研究。学生将来自不同的学术和文化背景，REU项目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；让学生为研究生院和跨学科团队中的行业机会做好准备。我们希望所有MAPs Drive for REU的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所在大学建立关系，以继续扩大研究合作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.