Networks are all around us. As such, network science is crucial for our understanding of many applications of high societal relevance (e.g., social and technological networks, epidemics, biological networks). Our research focuses on developing new machine learning methods to discover complex interactions and collective behaviors that determine how various types of events and behaviors in social networks are generated and propagated. In particular, we are interested in developing new approaches for social sensing that are relevant to the immediate concerns around pandemic detection and mitigation.
Selected Publications
Efficient modeling and simulation of bacteria-based nanonetworks with BNSim Journal Article
In: IEEE Journal on Selected Areas in Communications, vol. 31, no. 12, pp. 868–878, 2013.
Modeling populations of micro-robots for biological applications Proceedings Article
In: 2012 IEEE International Conference on Communications (ICC), pp. 6188–6192, IEEE 2012.
A fractional calculus approach to modeling fractal dynamic games Proceedings Article
In: 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 255–260, IEEE 2011.
Exploring congestion phase transitions in vehicular traffic via topology and driver behavior modeling Proceedings Article
In: 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, pp. 115–121, IEEE 2011.
On-chip networks: Two sides of the same coin Journal Article
In: IEEE Annals of the History of Computing, vol. 27, no. 4, pp. 80–80, 2010.
The Chip Is the Network Miscellaneous
2008.
Epidemics
Viral outbreaks spread throughout networks of people via transmission events. We aim to combine human mobility data, network science, and machine learning to inform and mitigate the disease dynamics for COVID-19. Furthermore, we aim to build an always-on social sensing system to improve a population’s resilience to a novel virus.
Social Networks
Objective social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. We are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. We aim to uncover new insights on understanding and engineering social media dynamics and their consequences on offline behaviors.
Biological Networks
It is well established that bacteria engage in social behavior and form networked communities via molecular signaling. We analyze the network dynamics and biofilm metrics, showing that our method can effectively reveal the underlying intercellular communication process and community organization within the biofilm. We claim that the application of social and network sciences to understanding bacteria population dynamics can aid in developing better drugs to control the many pathogenic bacteria that use social interactions to cause infections.