My research area is the relatively new field of network science (or complex networks). This means that I'm interested in complex systems with a large number of elements that are interacting with each other in some non-trivial way and possibly leading to some emergent phenomena. Social systems are a good example: they consist of multiple elements (people) that are interacting with each other (social relationships) and lead to some very complex emergent behaviour (social groups, societies, conflicts, etc.). Other such complex systems include transportation systems, gene-regulatory systems in cells, ecological systems and many more. I see all of these systems as networks that can be studied with the similar sets of tools and theories.

All of the above-mentioned systems, and many others, have been studied extensively using networks where the nodes (people, cities, genes, ...) are either connected by a pairwise link or not (friendship, road, regulatory relationship). This approach has been extremely successful. However, this abstraction discards a lot of important information about the system. For example, social networks have multiple types of relationships of varying strengths and they are inherently dynamic. Including such information greatly increases our understanding of these systems and processes on them, for example, how disease spreads on social networks. These types of more realistic networks are the focus point of my research.