Self-organization and assembly processes are crucial steps in the making of a wide range of materials and, in turn, have a great impact on their performance. For instance, the crystal structure, or polymorph, that forms during nucleation often dictates the bioavailability of pharmaceutical drugs, or the mechanical and catalytic properties of metal alloys and inorganic nanoparticles. In biology and medicine, protein folding and aggregation processes play a major role in the onset of many neurodegenerative disorders. Similarly, active, self-propelled, objects can form unexpected structures such as colloidal rotors on the micron scale, or bacterial biofilms, bird flocks and swarms of unmanned aerial systems on the macroscopic scale. While recent advances in experimental, theoretical & computational methods have allowed for unprecedented insights into the behavior of nonequilibrium systems, a complete understanding of these processes has remained elusive so far. For example, it is still impossible to predict which crystal structure forms when a liquid crystallizes. Similarly, the elucidation of the rules of life of swarms and active assemblies remains an outstanding challenge, although it is a necessary starting point to the successful development of soft matter robotics. In this talk, I discuss how my research group leverages computational materials science and artificial intelligence to shed light on assembly, cooperativity, and emergence in hard, soft and active matter. I show how recent advances in statistical mechanics and ML-guided simulations shed light on assembly pathways in materials and biological systems. I finally highlight how data science and machine learning methods provide a new way to accelerate discovery in soft autonomous robotics technology.