Topological Inference and Learning for Graphs

In this talk, Dr. Moo Chung presents novel topological inference and learning frameworks that can integrate networks of different sizes, topology or modalities through persistent homology. This is possible through the Kolmogorov-Smirnov and Wasserstein distances defined on persistent homological features. The methods are applied for 1) determining the conformational changes of the spike protein of COVID-19 virus, and 2) the heritability of functional brain networks.