Learning and Mining in Complex Networks, with Applications to Cyber Situational Awareness

Complex networks are ubiquitous in many domains. Examples include spatial, technological, informational, social, and biological networks. In this talk, I will present algorithms for both network classification and clustering, paying specific attention to evaluation in such non-IID settings, scalability, transfer learning, and applications to cyber situational awareness.

Tina Eliassi-Rad is an Assistant Professor at the Department of Computer Science at Rutgers University. She is also a member of the Rutgers Center for Computational Biomedicine, Imaging, and Modeling (CBIM) and Rutgers Center for Cognitive Science (RuCCS). Until September 2010, Tina was a Member of Technical Staff at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison in 2001. Broadly speaking, Tina's research interests include machine learning, data mining, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, and complex networks. Tina is an action editor for the Data Mining and Knowledge Discovery Journal. She received a US DOE Office of Science Outstanding Mentor Award in 2010.