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.
Pruning Digital Contact Networks for Meso-scale Epidemics Surveillance Using Foursquare Data Inproceedings Forthcoming
In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8399–8403, IEEE 2020.
In: arXiv preprint arXiv:2004.04222, 2020.
In: Bmc Bioinformatics, 20 (12), pp. 314, 2019.
In: Workshop on Network Interpretability for Deep Learning@ AAAI Conf. on Artificial Intelligence (AAAI), Honolulu, 2019.
Bot Detection in Reddit Political Discussion Inproceedings
In: Proceedings of the Fourth International Workshop on Social Sensing, pp. 30–35, 2019.