Session

DescriptionAdvances in computer science have brought us to the gates of the exascale era. In order to master this massive computational power, both industry and academia are investing their efforts in developing and improving new software tools and techniques. Some of these have been quickly adopted and widely used by the community, like big data and machine learning technologies. Many scientific domains benefit from the aforementioned software, which helps expanding knowledge in their study areas. For example, using big data technologies gives scientists the opportunity to tackle complex scientific questions that otherwise would be impossible to address. Furthermore, the combination of big data and machine learning has opened a whole new field of scientific possibilities. This minisymposium explores how different scientific domains (life sciences, physics) apply big data technologies and deep learning to bring their research one step forward. We focus on those cases where it would be impossible to address scientific questions without using big data, machine learning or a combination of both. We also emphasize the need of foreseeing the technical requirements behind the experiments and how the computational ecosystem needs to be in place well in advance to ensure the success of the scientific study.