Abstract: The rank of a matrix is the number of linearly independent rows (or columns) that the matrix has. Modeling using low-rank matrices typically works best for the many scenarios where data has high ambient dimension but low intrinsic dimension; indeed, methods like PCA, matrix factorization etc. are now bedrock methods in data analysis.
The talk will be accessible to a broad audience. Speaker: Sujay Sanghavi Ph.D is an Associate Professor in UT Austin. His interests lie in large-scale machine learning, and performance modeling of communication networks. He is a recipient of the NSF CAREER award and the DTRA young investigator award. He has also been a visiting scientist at Google and Qualcomm. Registration: Location: THE ADVISORY BOARD - BUILDING 7 (map - http://bit.ly/PA804c) Room Number: Suite 100 12357-C Riata Trace Parkway Bldg 7, Suite 100 Austin, Texas United States 78727 Meeting Agenda: 6:30 p.m. Networking and Gathering (with free food, drinks) 6:50 p.m. Call to Order, Announcement 7:00 p.m. Presentation, with Q/A 8:30 p.m. Meeting Evaluation, Adjourn |
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