Speaker: István Kovács, Ph.D., Northwestern University
Title: Characterizing Complex Patterns by Cluster Tomography
Complex patterns emerge in a broad range of systems, calling for efficient methods to characterize and compare them to other instances as well as to theoretical models. Examples include magnetic domains in classical and quantum systems, patterns of turbulence, various membrane structures, as well as vasculature and neuron anatomy. In this talk, I will discuss the concept of cluster tomography, an efficient geometric approach to characterize complex patterns. In the simplest scenario, cluster tomography is related to “chord-length statistics”. In stark contrast to standard approaches based on two-point correlations, these tools account for higher-order correlations, and offer a deep notion of distance between objects with complex shapes. First, I will present our analytic and numerical results for clustered classical and quantum systems, both in- and out-of-equilibrium, where cluster tomography unveiled universal insights. I will then discuss some of our ongoing collaborations on utilizing these tools in a biological setting, with applications focusing on biological membranes, blood vessels and neurons.