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College of Arts and Sciences News

Dr. Ruoming Jin Awarded a 5-year $523,859 NSF CAREER Grant

Posted Mar. 19, 2010

Dr. Ruoming Jin, Assistant Professor of Computer Science, has been awarded a 5-year $523,859 NSF CAREER grant. His project is entitled "CAREER: Novel Data Mining Technologies for Complex Network Analysis", and is described below.

CAREER: Novel Data Mining Technologies for Complex Network Analysis

In this project, Dr. Jin will develop novel data mining technologies to elucidate the structures and dynamics of complex but ubiquitous networks. A model complex network is a large system of elements (vertices) that are joined by non-trivial relationships (edges). Examples of such complex networks include the WWW, metabolic and protein networks, social networks, and economic and financial markets. The underlying principles and laws of these network systems can help construct more effective communication mechanisms, find cures for fatal diseases, and deal with economic crises.

Building upon an innovative blend of graph theoretical, information theoretical, and statistical learning concepts and techniques, the proposed mining methodologies in this project will 1) extract network backbones which both simplify and highlight network structures, 2) measure the network difference for comparative network analysis, and 3) apply causal inference to integrate time series with network topology. In a close collaboration with domain experts from bioinformatics, political science, and software engineering, the proposed techniques have the potential to help reveal the organizational principles of biocellular systems in a dynamic environment; identify therapeutic or drug targets; illuminate how large scale software systems form and evolve; and understand how human society is organized at the individual level (social networks) and organizational level (political science). Using the popular online social networks, such as MySpace and Facebook, as "hooks", this project will attract, recruit, and prepare students from underrepresented groups including women and minorities to computer science and involve underrepresented students in the cutting-edge research.