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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal>Bin Yu, University of California, Berkeley<o:p></o:p></p><p class=MsoNormal>Friday, March 23 – 1:20pm<o:p></o:p></p><p class=MsoNormal>Friend Center 101<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>“Three Principles of Data Science: Predictability, Stability and Computability”<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>This is part of the Conference on Information Sciences and Systems<o:p></o:p></p><p class=MsoNormal>More info here: <a href="http://ee-ciss.princeton.edu/">http://ee-ciss.princeton.edu/</a> <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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).<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>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).<o:p></o:p></p></div></body></html>