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.
INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip Inproceedings Forthcoming
In: 2022 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Forthcoming.
Quarantine in Motion: a Graph Learning Framework to Reduce Disease Transmission without Lockdown Inproceedings Forthcoming
In: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2022), Forthcoming.
In: ACM/IEEE Intl. Conf. on Advances in Social Network Analysis and Mining (ASONAM), 2021.
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, vol. 20, no. 12, pp. 314, 2019.