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Danai Koutra receives 2016 SIGKDD Doctoral Dissertation Award

The annual SIGKDD doctoral dissertation award recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery. | Short Read
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Prof. Danai Koutra has been awarded the 2016 SIGKDD Doctoral Dissertation Award for her dissertation, “Exploring and Making Sense of Large Graphs,” which she completed while a student at Carnegie Mellon University.

The annual SIGKDD doctoral dissertation award recognizes excellent research by doctoral candidates in the field of data mining and knowledge discovery.  Each year, SIGKDD receives over 15 nominations and only one winner is chosen.

In her dissertation, Prof. Koutra focuses on developing scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. She contributes graph-theoretical ideas and models, and real-world applications in two main areas: single-graph exploration and multi-graph exploration. She also leverages techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems.

Danai Koutra joined CSE in fall 2015 as an assistant professor. Broadly, her interests include large-scale graph mining, graph summarization, graph similarity and matching, and anomaly detection. Danai’s research has been applied to social, collaboration and web networks, as well as brain connectivity graphs. She has numerous papers in top data mining conferences, including 2 award-winning papers, and 7 current and pending patents on bipartite graph alignment. Danai has worked at IBM Research, Microsoft Research, and Technicolor Research Labs. She earned her Ph.D. and M.S. in Computer Science from Carnegie Mellon University in 2015 and her diploma in Electrical and Computer Engineering at the National Technical University of Athens in 2010.

Prof. Danai Koutra
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