Tina Eliassi-Rad, Assistant Professor, Computer Science Department
Learning and Mining in Large-Scale Complex Network
Tuesday, November 8, 4:00 PM
Reception prior to talk in Packard Lobby
Abstract: Complex networks are ubiquitous in many domains. Examples
include technological, informational, social, and biological networks. In this talk, I will present an overview of models, algorithms, and tools that we have developed for learning and mining in such networked data. I will pay special attention to issues surrounding scalability, sparsity of labels, various levels of relational dependency, and performance consistency across assorted domains.
Bio: Tina Eliassi-Rad is an Assistant Professor of Computer Science at Rutgers University. She earned her Ph.D. in Computer Sciences at the University of Wisconsin-Madison. Prior to joining Rutgers, Tina was a Member of Technical Staff at Lawrence Livermore National Laboratory. Broadly speaking, her research interests include data mining, machine learning, and artificial intelligence. Tina¹s work has been applied to the World-Wide Web, large-scale scientific simulation data, complex networks, and cyber situational awareness. Tina is an action editor for the Data Mining and Knowledge Discovery Journal. In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science.