Dr. MICHAEL JORDON
Pehong Chen Distinguished Professor
Departments of EECS and Statistics, AMP Lab, Berkeley AL Research Lab
University of California at Berkeley
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.
Wednesday, March 29, 2017, 3:30-4:30PM
On Computational Thinking, Inferential Thinking and Data Science
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in “Big Data” is apparent from their sharply divergent nature at an elementary level—in computer science, the growth of the number of data points is a source of “complexity” that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of “simplicity” in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as “runtime” in core statistical theory and the lack of a role for statistical concepts such as “risk” in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and methods for trading off the speed and accuracy of inference.
Chamber Room (on ground floor), Reitz Union