Carnegie Mellon University
Developing new measures, and as necessary, optimizing existing ones to analyze the structure and dynamics of large networks within a reasonable amount of time and developing approximation algorithms for network metrics that allow for estimating timeliness and fidelity.
Tackling the multi-layer bias problem of social media data to increase the value of these data for studying research questions of the “offline” world.
Developing visual analytics methods for network data that facilitate analytical reasoning by utilizing the human power to capture complex interrelations and revealing insights into human perception in the context of network drawings.
Developing stable methodological and theoretical foundations to apply network analysis methods to research problems in a very diverse array of scientific fields.