Networks and Learning
Our research lies in both basic and applied areas of networks, machine learning, and large-scale data analytics. We study fundamental issues and derive engineering solutions for modeling and managing heterogeneous large networks; develop learning algorithms; analyze and learn knowledge from large-scale data.
Recent Publications and News:
· Chuanyi Ji, Yun Wei, and H. Vincent Poor, “Resilience of Energy Infrastructure and Services: Modeling, Data Analytics and Metrics.” Proceedings of the IEEE - Special Issue of Power Grid Resilience. 105 (7), 1354-1366, July 2017.
Abstract: …What fundamental issues govern the resilience? Can advanced approaches such as modeling and data analytics help industry to go beyond empirical methods? ….
· Chuanyi Ji, Yun Wei, Henry Mei, Jorge Calzada, Matthew Carey, Steve Church, Timothy Hayes, Brian Nugent, Gregory Stella, Matthew Wallace, Joe White, & Robert Wilcox. Large-Scale Data Analysis of Power Grid Resilience across Multiple US Service Regions. Nature Energy, May 2016. (Cover-page article) DOI:10.1038/nenergy.2016.52
Abstract: … we analyse data from four major service regions representing Upstate New York during Super Storm Sandy and daily operations. Using non-stationary spatiotemporal random processes that relate infrastructural failures to recoveries and cost, our data analysis shows that local power failures have a disproportionally large non-local impact on people …
· “Large-Scale Data Analysis for Resilience of Power Distribution”, Panel on Big Data Analytics, PSE General Meeting, Boston, July, 2016. (Slides)
· “Resilience of Large-Scale Power Distribution”, Panel, Homeland Security Week, Oct. 2015.
· “Resilience of large-scale power distribution: Modeling and Real Data,” Special Session on Resilience of Power Grid, Applied Physics March Meeting, March 2015.
Contact: Chuanyi Ji
Lab: 5150 Centergy, 5th Street, Atlanta, GA30332
Office: 5165 Centergy, 5th Street, Atlanta, GA30332
Tel: 404 894 2393
Fax: 404 894 7883