| Title: | Three principles of data science: predictability, computability, and stability |
| Speaker: | Bin Yu, Departments of Statistics and EECS, UC Berkeley |
| Time: | 16:00-17:00, May 7, 2018 |
| Location: | Zibin N205, Fudan University |
| Abstract: | 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. Our DeepTune characterization demonstrate the diversity of V4 selection patterns. The second project proposes iterative random forests (iRF) as a stablized RF to seek predictable and interpretable high-order interactions among biomolecules. For an enhancer status prediction problem for Drosophila based on high-throughput data, iRF was able to find 20 stable gene-gene interactions, of which 80% had been physically verified in the literature in the past few decades. |
| 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 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. |