14–17 Oct 2019
Michael Mahoney

Michael Mahoney
Professor, UC Berkeley

Website

Michael W. Mahoney is a professor in the Department of Statistics and the International Computer Science Institute (ICSI) at the University of California, Berkeley. He works on the algorithmic and statistical aspects of modern large-scale data analysis. He’s also the director of the NSF/TRIPODS-funded Foundations of Data Analysis (FODA) Institute at UC Berkeley. Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. Previously, he worked and taught in the Mathematics Department at Yale University, at Yahoo Research, and in the Mathematics Department at Stanford University. Among other things, he’s on the national advisory committee of the Statistical and Applied Mathematical Sciences Institute (SAMSI), he was on the National Research Council’s Committee on the Analysis of Massive Data, he co-organized the Simons Institute’s fall 2013 and 2018 programs on the foundations of data science, and he runs the biennial MMDS Workshops on Algorithms for Modern Massive Data Sets. He earned his PhD from Yale University with a dissertation in computational statistical mechanics. More information is available at https://www.stat.berkeley.edu/~mmahoney/.

Sessions

11:0511:45 Thursday, 17 October 2019
Location: King's Suite - Balmoral
Secondary topics:  Deep Learning, Deep Learning tools
Michael Mahoney (UC Berkeley)
Average rating: ***..
(3.00, 4 ratings)
Developing theoretically principled tools to guide the use of production-scale neural networks is an important practical challenge. Michael Mahoney explores recent work from scientific computing and statistical mechanics to develop such tools, covering basic ideas and their use for analyzing production-scale neural networks in computer vision, natural language processing, and related tasks. Read more.

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