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

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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.