Linear Algebra for Data Science

Stat 89A with Michael W. Mahoney, Spring '20.


Michael W. Mahoney
mmahoney AT stat DOT berkeley DOT edu
I am at ICSI and the Department of Statistics at UC Berkeley, and I am also in the RISELab (in the past AMPLab) in the Department of EECS. Most of my work focuses on the theory and practice of what is now called big data, although I was doing it back when it was just massive, and prior to that when it was just large. On the theory side, we develop algorithmic and statistical methods for matrix, graph, regression, optimization, and related problems. On the implementation side, we provide implementations (e.g., on single machine, distributed data system, and supercomputer environments) of a range of matrix, graph, and optimization algorithms. On the applied side, we apply these methods to a range of problems in internet and social media analysis, social networks analysis, as well as genetics, mass spec imaging, astronomy, climate, and a range of other scientific applications. For more information, you can also see my CV; and here is a headshot and bio.
  • M 1-3 pm
  • W 10-12 pm