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.
S.C. Ganz, C. Duan, C. Ji, “Socioeconomic vulnerability and differential impact of severe weather-induced outages,” PNAS Nexus, Volume 2, Issue 10, October 2023, pgad295, https://doi.org/10.1093/pnasnexus/pgad295 (Report from Phys.org)
IEEE PES Task Force, AM Stanković, KL Tomsovic, F De Caro, M Braun, JH Chow, N Äukalevski, I Dobson, J Eto, B Fink, C Hachmann, D Hill, C Ji, JA Kavicky, V Levi, CC Liu, L Mili, R Moreno, M Panteli, FD Petit, G Sansavini, C Singh, AK Srivastava, K Strunz, H Sun, Y Xu, S Zhao, “Methods for Analysis and Quantification of Power System Resilience,” IEEE Transactions on Power Systems, P1-14, Oct. 2022 [PDF]
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] (Georgia Tech News from CoE, ECE, Research Horizon)