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: A.H. Afsharinejad, C. Ji and R. 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]