REU Site: Computational and Mathematical Modeling of Complex Systems

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The SFI Research Experiences for Undergraduates (REU) program is a ten-week residential research opportunity in which students develop innovative research projects in collaboration with mentors. The program asks students to discard traditional disciplinary boundaries, and learn computational modeling and data analysis techniques that can apply across the physical, natural, and social sciences. This allows students to ask big questions about real-world systems using rigorous mathematical and computational methods. Projects range from simulation to machine learning to proving theorems. The program supports the goals of science education and diversity in science by emphasizing engagement with students from non-elite institutions with limited research opportunities, women, and under-represented minorities (URMs). Early career scientists act as mentors in the program, gaining valuable experience as educators in mentoring. Research performed by SFI REUs has directly advanced the progress of science, and has focused on solving problems of direct relevance to society and the national health, including vaccination strategies for whooping cough, sustainability, economics of higher education, and social network analysis, among other topics. In every STEM field, computational and mathematical modeling are rapidly becoming essential skills: translating a real-world system into a quantitative model, designing and coding computational experiments, analyzing these experiments statistically, and comparing their results with data. Projects of this kind are an ideal opportunity for undergraduate training that builds students' technical and analytical skills, connects them with the wider scientific world, and links scientific thinking with real-world contexts and applications. The SFI REU program is designed around the strengths of being a leading transdisciplinary research center. Undergraduates are recruited from multiple departments including computer science, physics, mathematics, biology, and the social sciences, and paired with mentors from many different scientific backgrounds. Recent projects include epidemiology and public health, digital humanities and topic modeling, social network structure, cell biology and proteomics, smart cities and urban data, and statistical physics. Methods utilized range from simulation to data analysis to theorem-proving, and in many cases have produced publishable work. Throughout the summer, students are offered tutorials on the basics of data analysis, algorithms, network theory, statistics, and programming in Python and C++. Tutorials are also offered on science writing, presenting research, applying for jobs in science and picking a graduate school or industry carrer, and dealing with impostor syndrome and implicit bias.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.