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.
In: ACM Transactions on Embedded Computing Systems (TECS), vol. 18, no. 5s, pp. 1–22, 2019.
In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 102–114, Springer, Cham 2018.
In: Scientific reports, vol. 8, no. 1, pp. 1–14, 2018.
In: arXiv preprint arXiv:1807.08039, 2018.
In: Smart Water Grids, pp. 297–323, CRC Press, 2018.
Modeling computational, sensing, and actuation surfaces Journal Article
In: Low-Power Processors and Systems on Chips, pp. 16–1, 2018.