[talks] CISS Plenary Speaker Bin Yu, tomorrow at 1:20pm

[Emily Lawrence]

Bin Yu, University of California, Berkeley

Friday, March 23 - 1:20pm

Friend Center 101

 

"Three Principles of Data Science: Predictability, Stability and
Computability"

 

This is part of the Conference on Information Sciences and Systems

More info here: http://ee-ciss.princeton.edu/ 

 

In this talk, I'd like to discuss the intertwining importance and
connections of three principles of data science in the title. The three
principles will be demonstrated in the context of two neuroscience projects
and through analytical connections. In particular, the first project adds
stability to predictive models used for reconstruction of movies from fMRI
brain signals to gain interpretability of the predictive models. The second
project uses predictive transfer learning and stable (manifold) deep dream
images to characterize the difficult V4 neurons in primate visual cortex.
Our results lend support, to a certain extent, to the resemblance to a
primate brain of Convolutional Neural Networks (CNNs).

 

Bio: Bin Yu is Chancellor's Professor in the Departments of Statistics and
of Electrical Engineering & Computer Sciences at the University of
California at Berkeley. Her current research interests focus on statistics
and machine learning theory, methodologies, & algorithms for solving
high-dimensional data problems. Her lab is engaged in interdisciplinary
research with scientists from genomics, neuroscience, precision medicine and
political science. She obtained her B.S. degree in Mathematics from Peking
University and her M.A. and Ph.D. degrees in Statistics from the University
of California at Berkeley. She held faculty positions at the University of
Wisconsin-Madison and Yale University and was a Member of Technical Staff at
Bell Labs, Lucent. She was Chair of Department of Statistics at UC Berkeley
from 2009 to 2012, and is a founding co-director of the Microsoft Lab on
Statistics and Information Technology at Peking University, China, & Chair
of the Scientific Advisory Committee of the Statistical Science Center at
Peking University. She is Member of the U.S. National Academy of Sciences &
Fellow of the American Academy of Arts and Sciences. She was a Guggenheim
Fellow in 2006, an Invited Speaker at ICIAM in 2011, and the Tukey Memorial
Lecturer of the Bernoulli Society in 2012. She was President of IMS
(Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer
of IMS in 2016. She is a Fellow of IMS, ASA, AAAS and IEEE. She served on
the Board of Mathematics Sciences and Applications (BMSA) of NAS and as
co-chair of SAMSI advisory committee, & on the Board of Trustees at ICERM
and Scientific Advisory Board of IPAM. She has served or is serving on many
editorial boards, including Journal of Machine Learning Research (JMLR),
Annals of Statistics and American Statistical Association (JASA).

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