Fwd: [grads] Michael Jordan 4/8/16 Distinguished Lecture

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Haraldur Tómas Hallgrímsson

Apr 6, 2016, 4:38:36 PM4/6/16
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I'm sure this hasn't gone past anyone but this is an event you'll probably not want to miss. Michael Jordan is likely the biggest name in machine learning these days. He'll be giving a presentation 11am-12noon this Friday at Corwin Pavilion. PhD students he's advised that now also count as giants in the field include:

David Blei (inventor of LDA and Prof. at Columbia)
Yoshua Bengio (Deep neural network guru and Prof. at Montreal)
Andrew Ng (founder of Coursera and Prof. at Stanford)
Eric Xing (Prof. CMU)
Tommi Jaakola (Prof. MIT)


---------- Forwarded message ----------
From: Jennifer Martin <jma...@cs.ucsb.edu>
Date: Fri, Mar 11, 2016 at 11:52 AM
Subject: [grads] Michael Jordan 4/8/16 Distinguished Lecture
To: Typical faculty <fac...@lists.cs.ucsb.edu>, grads <gr...@lists.cs.ucsb.edu>, coll...@lists.cs.ucsb.edu, l...@engineering.ucsb.edu, Renee Meuret <re...@ece.ucsb.edu>, mate...@engineering.ucsb.edu, Shawnee Oren <or...@engineering.ucsb.edu>, Stella Hahn <stell...@lifesci.ucsb.edu>, Lizabeth Straight <li...@ece.ucsb.edu>, cl...@math.ucsb.edu, hay...@econ.ucsb.edu, CS Researchers <rese...@lists.cs.ucsb.edu>, Lecturers <lect...@lists.cs.ucsb.edu>, off...@lists.cs.ucsb.edu


On Computational Thinking,

Inferential Thinking and Data Science

Michael Jordan (University of California Berkeley)

Friday April 8, 2016

10:30 AM Reception

11:00 AM Talk

Corwin Pavilion

Host: Ambuj Singh



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.


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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Distinguished CS JORDAN lowres.pdf
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