Danai Koutra | ||
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CVEducation
Work Experience
for her Masters project with the CMU DB group on brain graph mining. Carnegie Mellon University, Graduate Research with Christos Faloutsos,Fall '10-present Currently, I am working on graph summarization, graph similarity, graph matching, compression, anomaly detection in graphs (using graph analytics or tensors), and data modeling. I have also worked on belief propagation on large heterogeneous graphs, and analysis of P2P data. I am interested in developing scalable algorithms for data mining tasks, and applying machine learning algorithms (e.g., belief propagation) in large, time-evolving graphs in order to find patterns and outliers. My goal is to develop algorithms that help "understanding large graphs". Research Intern at IBM TJ Watson Research Center,May-August '12 I interned at the Social Networks Analytics Group, and my mentor was Hanghang Tong. My work was on graph matching for data mining applications. Result: 1 paper under preparation, filing 7 patents. Mentoring Jay-Yoon Lee (Masters student) ,May-August '12 for his summer project with the CMU DB group on anomaly detection. Mentoring Cheng Chang (senior student) ,May-August '12 for her summer project with the CMU DB group on graph mining. Institute for the Management of Information Systems (IMIS) & National Technical University of Athens (NTUA)Spring '10 I conducted research for my diploma thesis. Teaching Experience
for Database Applications (15-415). Instructor: Christos Faloutsos. Carnegie Mellon University. Invited Talks
Conference and Journal Reviews
Grant Proposals
Graduate Coursework
15-812 Semantics of Programming Languages (Stephen Brookes) 10-704 Information Processing and Learning (Aarti Singh, project-based course - Project: On the intersenction between LDPC codes, Graphical Models and Belief Propagation) Fall 2011: 15-826: Multimedia Databases and Data Mining (Christos Faloutsos, research and project-based course - Project: Algorithms for Graph Similarity and Subgraph Matching) 10-705/36-705 Intermediate Statistics (Larry Wasserman) Spring 2011: 15-750 Graduate Algorithms (Manuel Blum) 15-712 Advanced and Distributed Operating Systems (Dave Andersen, research and project-based course - Project: Adaptive Routing in Friend-to-Friend Networks) Fall 2010: 15-781 Machine Learning (Aarti Singh, project-based course - Project: Belief Propagation as a Method of Classication in Heterogeneous Information Networks) 15-740 Computer Architecture (Onur Mutlu, research and project-based course - Project: Approximate Computing: Application Analysis and Hardware Design) Other Ph.D. Requirements
Awards and Honors
Technical Skills
Hobbiesswimming, fitness fusion, biking, hiking, canoeing, windsurfing, traveling, cooking, ski, tennis, dancing, reading |
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