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2016 Challis Lectures Given by Dr. Michael Jordan of the University of California at Berkeley
March 29, 2017 @ 3:30 pm - 4:30 pm
2016-2017 Challis Lectures
Dr. MICHAEL JORDAN
Pehong Chen Distinguished Professor
Departments of EECS and Statistics, AMP Lab, Berkeley AL Research Lab
University of California at Berkeley
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Dr. Jordan’s talk is co-sponsored by the Informatics Institute and the Department of Statistics
The Informatics Institute and the Department of Statistics at the University of Florida are 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.
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.
Michael Jordan’s research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA and SIAM.
Chamber Room (on ground floor), Reitz Union