Prior to joining BU, Daniel served as a postdoctoral fellow at Harvard University for two years. He received his PhD in applied math and statistics from Johns Hopkins University in 2014. Daniel develops theory and methods for studying network data. He has studied spectral methods and other tools to embed the nodes of a network in Euclidean space. He is also interested in tools to study multiple networks simultaneously, especially when the networks arise from disparate sources. Finally, he researches how to perform and analyze experiments on networked groups of individuals.