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.
Non-Stationary Bayesian Learning for Global Sustainability Journal Article
In: IEEE Transactions on Sustainable Computing, 2 (3), pp. 304–316, 2017.
In: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473 (2203), pp. 20170154, 2017.
In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 168–177, 2017.
Molecular communication with DNA cellular storage system Inproceedings
In: Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication, pp. 1–6, 2017.
In: International Conference on Discovery Science, pp. 223–238, Springer, Cham 2017.
In: PLoS computational biology, 13 (12), pp. e1005915, 2017.