Michael Mahoney
homepage:http://cs.stanford.edu/people/mmahoney/
search externally:   Google Scholar,   Springer,   CiteSeer,   Microsoft Academic Search,   Scirus ,   DBlife

Description

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.


Lectures:

lecture
flag Linear Algebra and Machine Learning of Large Informatics Graphs
as author at  Numerical Mathematics Challenges in Machine Learning,
732 views
  tutorial
flag Geometric Tools for Graph Mining of Large Social and Information Networks
as author at  Tutorials,
842 views
invited talk
flag Statistical Leverage and Improved Matrix Algorithms
as author at  Workshops,
315 views