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.
Selected Publications
NIF: A Framework for Quantifying Neural Information Flow in Deep Networks Inproceedings
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.
Memory-and communication-aware model compression for distributed deep learning inference on iot Journal Article
In: ACM Transactions on Embedded Computing Systems (TECS), vol. 18, no. 5s, pp. 1–22, 2019.
Dimensionality reduction via community detection in small sample datasets Inproceedings
In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 102–114, Springer, Cham 2018.
Weighted betweenness preferential attachment: A new mechanism explaining social network formation and evolution Journal Article
In: Scientific reports, vol. 8, no. 1, pp. 1–14, 2018.
PGLasso: Microbial Community Detection through Phylogenetic Graphical Lasso Journal Article
In: arXiv preprint arXiv:1807.08039, 2018.
Epidemics
Viral outbreaks spread throughout networks of people via transmission events. We aim to combine human mobility data, network science, and machine learning to inform and mitigate the disease dynamics for COVID-19. Furthermore, we aim to build an always-on social sensing system to improve a population’s resilience to a novel virus.
Social Networks
Objective social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. We are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. We aim to uncover new insights on understanding and engineering social media dynamics and their consequences on offline behaviors.
Biological Networks
It is well established that bacteria engage in social behavior and form networked communities via molecular signaling. We analyze the network dynamics and biofilm metrics, showing that our method can effectively reveal the underlying intercellular communication process and community organization within the biofilm. We claim that the application of social and network sciences to understanding bacteria population dynamics can aid in developing better drugs to control the many pathogenic bacteria that use social interactions to cause infections.