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Make Data Work
Feb 17–20, 2015 • San Jose, CA

On the Computational and Statistical Interface and "Big Data"

Michael Jordan (UC Berkeley)
9:45am–10:30am Wednesday, 02/18/2015
Hardcore Data Science
Location: LL20 BC.
Average rating: *****
(5.00, 3 ratings)

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 statistical 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. We wish to blend these perspectives. In this talk we show how statistical decision theory provides a mathematical point of departure for achieving such a blending. We develop theoretical tradeoffs between statistical risk, amount of data and “externalities” such as computation, communication and privacy. We develop procedures that allow one to choose desired operating points along such tradeoff curves. [Joint work with Venkat Chandrasekaran, John Duchi, Martin Wainwright and Yuchen Zhang.]

Photo of Michael Jordan

Michael Jordan

UC Berkeley

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.
He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His 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 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.

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Ahmed Soliman
02/18/2015 3:22am PST

Can you please share the presentation slides?

Thanks!