REU Site: Intelligent and Scalable Systems (Renewal)

The Intelligent and Scalable Systems Research Experiences for Undergraduates (REU) site provides a 10-week intensive research experience for 10 undergraduate students per year. Participants will conduct cutting-edge research with mentorship from faculty focusing on the intersection between two critical research areas: machine learning and scalable systems.

During the course of their research projects, students will learn how to apply machine learning to create systems that learn from data and apply what they have learned to produce solutions to pressing societal and technical problems. Through a focus on scalability, the REU site will ensure that students are able to produce research artifacts that can make use of parallel computational resources, in order to process data quickly without sacrificing accuracy.

The REU Site will recruit broadly, with an emphasis on encouraging women, underrepresented, and minority students to participate. The REU Site will also offer an extensive program of seminars and tutorials that ensure students have the necessary background in machine learning, concurrency, and research methods, not only for the sake of their summer projects, but also to prepare them for advanced career options and graduate education.

The primary goal of this REU Site will be to mentor students and develop them into competent and successful researchers who are inspired to pursue graduate degrees in computer science. Participants will conduct research in fundamental topics in both machine learning and scalable computer systems. Examples include new algorithms for training of machine learning models, algorithms and techniques for increasing the speed of processing training data, acceleration of inference, techniques for enhancing the explainability of intelligent systems, and application of machine learning to hard scientific and societal problems.

Students will participate in a broad series of seminars and workshops that will emphasize ethical use of machine learning; new techniques for efficient computation and software development; technical communication; novel applications of machine learning; the research process and methodology; preparation for graduate studies; and how to succeed in a career in Computer Science.

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.