Many phenomena in the life and social sciences, ranging from the microscopic to macroscopic level, exhibit surprisingly similar structures. Processes at the microscopic level such as ion chan- nel transport, chemotaxis, cell adhesion, cytoskeleton dynamics and angiogenesis, and patterns at the macroscopic level which involve herding of animal populations, motion of human crowds, human interactions, cell shape and motion and alignment of bacteria are both largely driven by long-range attractive forces, due to electrical, chemical or social interactions, and short- range repulsion, due to cell finite size or crowding effects together with alignment forces. This common modelling framework based on individual-based models (IBMs) was classically used in other many-body systems such as electron transport in semiconductors, kinetic equations in statistical physics and gravitational collapse. Yet, its successful application in mathematical biology, social sciences and machine learning has only recently received a great deal of atten- tion. Indeed, this modelling strategy is currently being explored as a test bed for algorithms in data science and global optimization.