Computer Science Events
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Computer Science
[PAST EVENT] Computer Science Colloquium: Numerical Linear Algebra Methods in Data Mining
October 31, 2014
3pm
Yousef Saad, University of Minnesota
Abstract:
The field of data mining is the source of many new, interesting, and sometimes challenging, linear algebra problems. In fact, one can say that data mining and machine learning are now beginning to shape a "new chapter" in numerical linear algebra, replacing Computational Fluid Dynamics and PDEs as the main source of `model' problems in Numerical Linear Algebra. The talk will start with an overview of the key concepts and then discuss dimension reduction methods which play a major role. We will illustrate these concepts with a few applications, including information retrieval, face recognition and matrix completion for recommender systems. An important emerging application is 'materials informatics'. The synergy between high-performance computing, efficient electronic structure algorithms, and data mining, may potentially lead to major discoveries in materials. We will report on our first experiments in 'materials informatics', a methodology which blends data mining and materials science.
Bio:
Yousef Saad is an I.T. Distinguished Professor of Computer Science in the Department of Computer Science and Engineering at the University of Minnesota. He holds the William Norris Chair for Large-Scale Computing since January 2006. He is known for his contributions to the matrix computations, including the iterative methods for solving large sparse linear algebraic systems, eigenvalue problems, and parallel computing. Prof. Saad is listed as an ISI highly cited researcher in mathematics and is the author of the influential GMRES method and the highly cited book 'Iterative methods for sparse linear systems'.
Abstract:
The field of data mining is the source of many new, interesting, and sometimes challenging, linear algebra problems. In fact, one can say that data mining and machine learning are now beginning to shape a "new chapter" in numerical linear algebra, replacing Computational Fluid Dynamics and PDEs as the main source of `model' problems in Numerical Linear Algebra. The talk will start with an overview of the key concepts and then discuss dimension reduction methods which play a major role. We will illustrate these concepts with a few applications, including information retrieval, face recognition and matrix completion for recommender systems. An important emerging application is 'materials informatics'. The synergy between high-performance computing, efficient electronic structure algorithms, and data mining, may potentially lead to major discoveries in materials. We will report on our first experiments in 'materials informatics', a methodology which blends data mining and materials science.
Bio:
Yousef Saad is an I.T. Distinguished Professor of Computer Science in the Department of Computer Science and Engineering at the University of Minnesota. He holds the William Norris Chair for Large-Scale Computing since January 2006. He is known for his contributions to the matrix computations, including the iterative methods for solving large sparse linear algebraic systems, eigenvalue problems, and parallel computing. Prof. Saad is listed as an ISI highly cited researcher in mathematics and is the author of the influential GMRES method and the highly cited book 'Iterative methods for sparse linear systems'.