Electrical and Computer Engineering
Dean of Engineering
Professor of Electrical and Computer Engineering
University of California, Berkeley
ECE Brice Colloquium: On Computational Thinking, Inferential Thinking and Data Science, Dr. Michael Jordan UC Berkeley (698/699)
Wednesday, March 9, 2016
to 5:00 PM
McMurtry Auditorium Duncan Hall
6100 Main St
Houston, Texas, USA
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.
The Gene Brice Colloquium Series is supported by contributions to the Gene Brice Colloquium Fund. The Gene Brice Colloquium Fund for Electrical Engineering was established in 1991 in memory of William E. (Gene) Brice, B.S.E.E. '37.
A reception in Martel Hall will follow the talk.
Biography of Michael Jordan:
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.
His research in recent years has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in signal processing, statistical genetics, computational biology, information retrieval and natural language processing. Prof. Jordan was elected a member of the National Academy of Sciences (NAS) in 2010, of the National Academy of Engineering (NAE) in 2010, and of the American Academy of Arts and Sciences in 2011. He is a Fellow of the American Association for the Advancement of Science (AAAS). He has been named a Neyman Lecturer and a Medallion Lecturer by Institute of Mathematical Statistics (IMS). He is a Fellow of the IMS, a Fellow of the IEEE, a Fellow of the AAAI, and a Fellow of the ASA.