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.
Is “Network” the Next “Big Idea” in Design? Proceedings Article
In: Proceedings of the Design Automation & Test in Europe Conference, pp. 1–3, IEEE 2006.
In: IEE Proceedings-Computers and Digital Techniques, vol. 152, no. 5, pp. 643–651, 2005.
Adaptive data partitioning for ambient multimedia Proceedings Article
In: Proceedings of the 41st annual Design Automation Conference, pp. 562–565, 2004.