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Seminar
Department of Systems Engineering and Engineering ManagementTitle : Low-Rank Tensor Optimization Problems
Speaker : Prof. Shiqian Ma--------------------------------------------------------------------------------------------
Abstract:
In this talk, we will discuss several tensor optimization problems that require the resulting solution to have a low-rank structure. These problems include low-rank tensor completion, low-rank and sparse tensor separation and tensor principal component analysis. We show that although these problems are all nonconvex and NP-hard in general, they can be approximated very well in practice by certain structured convex optimization problems. Numerical results on applications arising from computer vision and portfolio selection with higher-order moments will be presented.
Biography:
Shiqian Ma received his B.S. from Peking University in 2003, M.S. from Chinese Academy of Sciences in 2006 and Ph.D. in Industrial Engineering and Operations Research from Columbia University in 2011. He then spent one and half years in the Institute for Mathematics and Its Applications at University of Minnesota as an NSF postdoctoral fellow. Dr. Ma joined the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong in December 2012. His current research interests include theory and algorithms for large-scale optimization and its applications in statistics, machine learning and signal/image processing.
Dr. Ma is a recipient of the INFORMS Optimization Society best student paper prize in 2010, honourable mention of INFORMS George Nicholson student paper competition in 2011, and one of the finalists of the 2011 IBM Herman Goldstine fellowship.
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