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.
Adaptive data partitioning for ambient multimedia Inproceedings
In: Proceedings of the 41st annual Design Automation Conference, pp. 562–565, 2004.
Pruning Digital Contact Networks for Meso-scale Epidemic Surveillance Using Foursquare Data Inproceedings Forthcoming