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Communication-Avoiding Statistical Inference

  • Reitz Union Chamber Room (map)

Dr. MICHAEL JORDON

Pehong Chen Distinguished Professor
Departments of EECS and Statistics, AMP Lab, Berkeley AL Research Lab
University of California at Berkeley

Overview

The Department of Statistics at the University of Florida is pleased to announce that the 2016-2017 Challis Lectures will be given by Michael Jordan of the University of California, Berkeley. This year the Challis Lectures will be given in the Chamber on the ground floor of the Reitz Union (REI). Refreshments will be served 30 minutes beforehand in room REI G315. The first of the two Challis lectures is usually aimed at a broader scientific audience, while the second lecture may be more technical and specialized.

Thursday, March 30, 2017, 2:30-3:30PM

Communication-Avoiding Statistical Inference

Modern data analysis increasingly takes place on distributed computing platforms. In the distributed setting, procedures that minimize communication among processors can be orders-of-magnitude faster than naive procedures. This fact has revolutionized numerical linear algebra, but it has yet to have significant impact on statistics. I discuss communication-avoiding approaches to statistical inference, including a novel form of the bootstrap, a primal-dual approach to M-estimation, a surrogate likelihood framework and distributed forms of false discovery rate control. 

Location

Chamber Room (on ground floor), Reitz Union