REU Site: Computational Mathematics for Data Science

Emory University, Atlanta GA

Investigators

Abstract

The Emory Research Experience for Undergraduates site focuses on computational mathematics and its applications in data science. Data science is of fundamental and strategic importance to the US and impacts nearly every field of science. However, the number of academic training opportunities and skilled workers has not kept pace with the rapid growth in demand from private and public entities. This REU site emphasizes developing research and professional skills to increase students’ ability to understand, conduct, and effectively communicate data science and computational mathematics research. The three-year program will train twelve undergraduates for eight weeks each summer. Faculty members from Emory's Departments of Mathematics and Computer Science Departments will supervise and mentor the students. The site's activities seek to motivate undergraduate students to pursue a graduate degree in this area and equip them with the mathematical and computational skills required to launch careers in academia and industry. Student recruitment will be nationwide, focusing strongly on underrepresented groups and students enrolled in colleges with limited research opportunities. The REU site will introduce undergraduate students to the mathematical theory of computational and machine learning tools and help them tackle open problems in mathematics and related areas. Mathematical breakthroughs in theory, models, and computational methods are needed to extract knowledge from increasingly large and complex data sets and harvest the potential of artificial intelligence to lead to new solutions and discoveries. This REU site will enable teams of undergraduate students to contribute at the interface between computational mathematics and data science. This REU site's annual research themes are broad and consist of learning in imaging science (2024), combining models with data (2025), and discovering new mathematics with computational and machine learning methods (2026). Students will be introduced to various mathematical techniques, including machine learning, deep neural networks, numerical and symbolic computing, optimization, differential equations, and formal mathematics. The projects are designed to promote learning and create new insights of relevance to the scientific community. These insights will be developed and disseminated in student-authored publications, open-source software, and online blogs. The weekly seminar will also provide their mentees with professional mentorship and research skills, including scientific writing, oral and poster presentations, and cloud computing. More information about the site can be found at http://www.math.emory.edu/site/cmds-reuret/. 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.