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Michael Mahoney, is currently at Stanford University. His research interests focus on theoretical and applied aspects of algorithms for large-scale data problems in scientific and Internet applications. Currently, he is working on geometric network analysis methods; developing approximate computation and regularization methods for large informatics graphs; and applications to community detection, clustering, learning, and information dynamics in large social and information networks. In the past, he has worked on the design and analysis of randomized algorithms for matrices, as well as applications of those methods in genetics and medical imaging. He has been a faculty member at Yale University and a researcher at Yahoo, and his PhD was is computational statistical mechanics at Yale University.
Linear Algebra and Machine Learning of Large Informatics Graphs
as author at Numerical Mathematics Challenges in Machine Learning,
Geometric Tools for Graph Mining of Large Social and Information Networks
as author at Tutorials,
Statistical Leverage and Improved Matrix Algorithms
as author at Workshops,