Predictive Modeling of Information Networks by Exploiting Heterogeneous Knowledge while Learning from Data
In structured regression on networks, the response is predicted at multiple nodes given explanatory variables while taking into account the network’s structure. This problem is challenging when there are multiple kinds of dependencies among nodes, some influences are negative and others are positive, uncertainties propagate for longer term predictions and representation of attributes and dependencies among nodes are learned jointly. Our solutions for these problems proposed at 4 papers published at AAAI-16 and SDM-16 will be discussed in this presentation.
About the lecturer
Zoran Obradovic an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He 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 Department of Statistical Science, and is the Director of the Center for Data Analytics and Biomedical Informatics. His research interests include data science 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 chaired the SIAM Activity Group on Data Mining and Analytics. His work is published in more than 320 articles and is cited about 16,500 times (H-index 48).