Omidyar Postdoctoral Fellow

Networks are key to describing many phenomena around and within us. From the complex signaling in the mammalian neocortex, to the patterns generated by genetic recombination, networks provide a strong foundation on which to build analytical tools that are capable of making testable predictions. However, to move beyond confirmation of known patterns into the territory of new discoveries, network techniques must be rigorously derived and rooted in strong theory. The findings of network techniques that do not overlap with those made using traditional tools are, in Daniel's view, the most valuable, and this motivates his dual focus on application and theory.

Daniel's ongoing applied work includes the evolution of antigenic variation of malaria parasites—variation which allows them to evade the human immune system almost indefinitely. He studies this ongoing evolution in human hosts as well as its roots in chimpanzee and gorilla parasites. He also continues to examine hierarchy and its emergence in university faculty hiring networks, from the concepts of prestige to gender inequality. Finally, he studies the dynamics of the mammalian neocortex, which appear to be positioned precisely at the critical point of a phase transition at which properties such as dynamic range and information entropy are maximized. From a purely theoretical perspective, Daniel's work focuses on clarifying the widely used and misused configuration model, as well as community detection using the stochastic block model.

Prior to the Omidyar Fellowship, Daniel earned a B.S. in chemical engineering from Washington University in St. Louis, a M.S. and a Ph.D. in Applied Mathematics from the University of Colorado at Boulder, and joined the Center for Communicable Disease Dynamics in the Department of Epidemiology at the Harvard School of Public Health as a postdoctoral fellow. His research has been supported by the National Science Foundation and the National Institutes of Health.