Zoran Obradović is a L.H. Carnell Professor of Data Analytics at Temple University, Professor in the Department of Computer and Information Sciences with a secondary appointment in Statistics, and is the Director of the Center for Data Analytics and Biomedical Informatics. His research interests include data mining and complex networks applications in health management and other complex decision support systems. Zoran is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at eleven journals. He was general co-chair for 2013 and 2014 SIAM International Conference on Data Mining and was the program or track chair at many data mining and biomedical informatics conferences. In 2014-2015 he chairs the SIAM Activity Group on Data Mining and Analytics. His work is published in about 300 articles and is cited more than 14,000 times (H-index 46). For more details see http://www.dabi.temple.edu/~zoran/
Predictive Analysis in Complex Dynamic Networks
Node attributes and links in information networks often evolve over time and are inextricably dependent on each other. In addition, the evolving network is partially observed, multiple kinds of links exist among nodes and various nodes have different temporal dynamics. In this talk we will present an overview of the results of our ongoing DARPA GRAPHS big data project aimed to address these challenges by developing a structured learning Gaussian conditional random field (GCRF) model, which is shown to be more accurate than several unstructured alternatives in forecasting dynamics of evolving complex networks.