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SUMMARY:PhD Thesis Proposal - Ang Li
DESCRIPTION:PhD Thesis Proposal - Ang Li\n
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Committee Members: T. K. Satish Kumar (chair), Sven Koenig, Aiichiro Nakano, Emilio Ferrara, and John Carlsson\n
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Title: Revisiting FastMap: New Applications\n
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Abstract: FastMap was first introduced in the Data Mining community for generating Euclidean embeddings of complex objects. In this talk, I will first generalize FastMap to generate Euclidean embeddings of graphs in near-linear time: The pairwise Euclidean distances approximate a desired graph-based distance function on the vertices. I will then apply the graph version of FastMap to efficiently solve various graph-theoretic problems of significant interest in AI: including shortest-path computations, facility location, top-K centrality computations, and community detection and block modeling. I will also present a novel learning framework, called FastMapSVM, by combining FastMap and Support Vector Machines. I will then apply FastMapSVM to predict the satisfiability of Constraint Satisfaction Problems and to classify seismograms in Earthquake Science
DTSTART:20231024T140000
LOCATION:EEB 110
URL;VALUE=URI:https://usc.zoom.us/j/92891703811?pwd=MmhNQXJCY3ZhMTRlOGp0aWpBZkRsZz09
DTEND:20231024T153000
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