Summary
Network data, which captures relationships and interactions among entities, is central to many modern AI and machine learning applications in areas such as neuroscience, social science, economics, and biomedicine. Examples include brain connectivity networks, social interaction graphs, and recommendation systems. This project develops new machine learning and statistical methods for analyzing complex network data, with a focus on prediction, representation learning, and comparing populations of networks. The project emphasizes interpretable and reliable AI methods that quantify uncertainty, pr
