​​CRANE - A Case Study of an Inclusive Approach to Recruit the Next Generation Computational Research Scientists

Modern scientific research relies heavily on a number of highly specialized computational methods and simulation codes. In order to engage in theoretical physics and chemistry, material science, and engineering research, basic knowledge of scientific computing is essential. In general, many of these essential techniques are only taught at the graduate level, if at all. As a result, this knowledge gap prevents potential undergraduate researchers from pursuing computational scientific research and further disadvantages students who are historically underrepresented in the field (e.g., BIPOC, LGBTQ+, and first-generation/low-income students).

The goal of the Computational Research Access NEtwork (CRANE) is to diversify the next generation of scientists by providing underrepresented groups access to a broad set of computational tools that will prepare them to engage in computational research while also providing representation from marginalized communities. The CRANE workshop series focuses on building a Python-based computational package that includes simulation methods, algorithms such as Monte Carlo and differential equation solvers, and data-driven methods. This presentation will discuss the data and findings from the second year of operation of CRANE.