This interdisciplinary REU Site program will prepare undergraduates for research, employment, and advanced study in the emerging discipline of Computational Science - a discipline that is critical not only for the advancement of science, but for our nation's economic growth and security. With the advent of more powerful computing tools, computational science is often described as the third pillar of science, joining the traditional pillars of theory and experiment. Students will work in small groups and with faculty guidance while employing innovative computational tools to solve problems in applied science. Students will acquire essential technical and professional skills by participating in faculty-led workshops. One-on-one interactions with faculty mentors, collaboration between students, independent inquiry, and guided training will provide students with valuable skills in programming, computational modeling, simulation, data analysis, visualization, research management, and communication. The research conducted through this REU Site program has the potential to produce high-impact, publication-quality results in the fields of materials science, epidemiology, cellular biology, and molecular dynamics. Targeted recruiting and selection efforts will ensure that under-represented constituencies, including women, minority, first generation college, and veteran students, are well represented in the program. The objective of this REU Site program is to recruit undergraduate students into the fast growing, and interdisciplinary field of computational science. Faculty will mentor students as they integrate computation and mathematical/statistical theory to solve problems in molecular dynamics, epidemiology, materials science, and cellular biology. Research problems will form and test mechanistic hypotheses about the structure of biological proteins, yield insights into the dynamics and control of diseases that pose a major threat to public health, address computational topics in the design of optical and acoustical metamaterials, and investigate fundamental principles of cellular decision-making. These research projects, together with a series of bi-weekly workshops, will support innovation by training students in a) Python programming, including best practices for readability, documentation, sharing, and structuring of code and b) tools (e.g. MDAnalysis, Comsol, and Pymol) and techniques (e.g., machine learning, sensitivity analysis, and parameter estimation) for model development, data analysis, and visualization. Students with a strong background in mathematics, computer science, physics, or biology, and an intense interest in applied science and computation are encouraged to apply. 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.