7月7日郁彬(美国国家科学院院士)学术报告
 
  发布时间: 2017-07-06  
 




学术报告


报告题目:Three principles of data science: predictability, stability,

and computability

报 告 人:郁彬

美国艺术与科学学院院士

美国国家科学院院士

加州大学伯克利分校统计系和电子工程与计算机科学系终身教授

时    间:77(星期五)上午 8:45-9:30

地    点:汇高花园酒店 花园厅

摘    要: In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results (cf. Yu, 2013). It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. 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 modeling used for reconstruction

of movies from fMRI brain signlas for interpretable models. The second project use predictive transfer learning that combines AlexNet, GoogleNet and VGG with single V4 neuron data for state-of-the-art prediction performance.  Our results lend support, to a certain extent, to the assemblence of these CNNs to brain and at the same time provide stable pattern interpretations of neurons in the difficult primate visual cortex V4.


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统计研究院

报告人简介:

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, and algorithms for solving high-dimensional data problems. Her group 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 in 1984, her M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley in 1987 and 1990, respectively. 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, and 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 and 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, and 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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