Danai Koutra is an Assistant Professor in Computer Science and Engineering at University of Michigan, where she leads the Graph Exploration and Mining at Scale (GEMS) Lab. She earned her M.S. and PhD in Computer Science from CMU, and won the 2016 ACM SIGKDD Dissertation award. She is the Program Director of the SIAG on Data Mining and Analytics, an Associate Editor of ACM TKDD, and has served in the organizing committees of the top-tier data mining conferences.
Koutra focuses on principled, practical, and scalable graph mining methods for graph or network data, which are ubiquitous in the real world. The problems that she researches involve three key challenges: the large size of graph datasets thanks to advances in data generation and storage; the continuous evolution of graphs over time; and the need to jointly analyze multiple networks (e.g., in social and biological sciences).
Koutra's research interests include large-scale graph mining, graph summarization, representation learning, analysis of multi-source network data, network similarity and alignment, and anomaly detection. Applications of her work span from analysis of social, collaboration and web networks to brain (functional) connectivity graphs.