Carlos Cordoba

PhD students, postdocs and independent researchers often struggle when trying to execute code developed locally in the cloud or HPC clusters for better performance. This is even more difficult if they can't count on IT staff to set up the necessary infrastructure for them on the remote machine, which is common in third-world countries.

One of the main features of scientific programming is its exploratory nature: starting from some input data, the goal is to analyze it in order to understand what it can tell us about the phenomena that generated it. However, the means to do this are often unclear, and the results unforeseen.