Our research is on large-scale data analytics and machine learning for networks. Our research seeks real-world applications important to our society. These problems motivate new methodologies on data analytics and learning in a networked setting. Recent work includes
- Resilient energy networks and impact on people,
- Spatial temporal models for learning from micro data at scale.
In collaboration with colleagues, service providers and policy makers, our research uses field data from large geographical regions in the United States. We welcome collaboration.
Recent publication and news
Amir Afsharinejad, Chuanyi Ji and Robert Wilcox, “Large-scale data analytics for resilient recovery services from power failure,” Joule – Cell Press, Volume 5, Issue 9, P2504-2520, Sept. 2021 (featured article) [PDF]
Abstract: Massive power failures are induced frequently by natural disasters. A fundamental challenge is how recovery can be resilient to the increasing severity of disruptions in a changing climate. We conduct a large-scale study on recovery from 169 failure events at two operational distribution grids in the states of New York and Massachusetts. Guided by unsupervised learning from non-stationary data, our analysis finds that under the widely adopted prioritization policy favoring large failures, recovery exhibits a scaling property where a majority (90%) of customers recovers in a small fraction (10%) of total downtime. However, …