Seminar Series: Graph Models and Scalable Analytics, October 22, 2013

Graphs are a natural representation of relationships between various types of objects, which arise in many applications, such as the web, social networks, business intelligence, information retrieval, and computer security. The increasing volume and complexity of data motivates the need for scalable analytics that can answer key questions such as: (i) which are the most important nodes? or (ii) what are the key communities of nodes? In this talk, we motivate and discuss various graph models and then present scalable analytics for three important classes of problems.

Title: Graph Models and Scalable Analytics

Speaker: Spiros Papadimitriou, Rutgers University

Date: Tuesday, October 22, 2013 11:00am – 12:00pm

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, Busch Campus, Piscataway, NJ

 

 

 

Bio:

Spiros Papadimitriou has worked extensively on scalable models and analytics in several domains, including graphs, as well as timeseries, spatial, and streaming data. He has published more than forty five papers on these topics in refereed conferences and journals. He has three invited publications in best paper journal issues, several book chapters, and has filed multiple patents. His interests in data analysis span from the very large (large-scale data processing and analysis; Hadoop) to the very small (sensors and embedded devices; Arduino), and he is also interested in mobile applications and has contributed to open source projects. He was a 2005 Siebel scholarship recipient and received the best paper award in SDM 2008. He has also been invited to give keynote talks on graph and social network analysis, and tutorials on time series stream mining and large-scale mining with Hadoop. He is currently an assistant professor at Rutgers University (MSIS-RBS). Prior to that, he was a research scientist at Google, and a research staff member at IBM T.J. Watson. He received hisPhD in computer science from Carnegie Mellon University.

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