Date(s) - 28/02/2018
11:00 am - 12:00 pm
Studio Villa Bosch
Three Principles of Data Science: Predictability, Stability, and Computability
By Bin Yu, Departments of Statistics and EECS, UC Berkeley, USA
In this talk, I’d like to discuss the intertwining importance and connections of three principles of data science in the title.They will be demonstrated in the context of two collaborative projects in neuroscience and genomics, respectively. The first project in neuroscience uses transfer learning to integrate fitted convolutional neural networks (CNNs) on ImageNet with regression methods to provide predictive and stable characterizations of neurons from the challenging primary visual cortex V4.
The second project proposes iterative random forests (iRF) as a stabilized RF to seek predictable and interpretable high-order interactions between biomolecules.
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, and precision medicine.
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 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, 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.
Watch the movie of the talk including slides:
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