Video-DSDLS-20161003
-
- Last edited 2 years ago by Alexander Taubman
-
Bin Yu
Theory to Gain Insight and Inform Practice
Henry L. Rietz, the first president of IMS, published his book “Mathematical Statistics” in 1927. One review wrote in 1928:“Professor Rietz has developed this theory so skillfully that the ’workers in other fields’, provided only that they have a passing familiarity with the grammar of mathematics, can secure a satisfactory understanding of the points involved.”In this lecture, I would like to promote the good tradition of mathematical statistics as expressed in Rietzs book in order to gain insight and inform practice. In particular, I will recount the beginning of our theoretical study of dictionary learning (DL) as part of a multi-disciplinary project to “map a cell’s destiny” in Drosophila embryo. I will share insights gained regarding local identifiability of primal and dual formulations of DL. Furthermore, comparing the two formulations is leading us down the path of seeking confidence measures of the learned dictionary elements (corresponding to biologically meaningful regions in Drosophila embryo). Finally, I will present preliminary work using our confidence measures to identify potential knockout (or gene editing) experiments in an iterative interaction between biological and data sciences.
Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley and a former Chair of Statistics at Berkeley. She is founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking, and develops statistical and machine learning algorithms and theory. She has published over 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks.
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, the Rietz Lecture of Institute of Mathematical Statistics (IMS) in 2016. She was IMS President in 2013-2014, and is a Fellow of IMS, ASA, AAAS and IEEE. She has served or is serving on leadership committees of NAS-BMSA, SAMSI, IPAM and ICERM, and editorial boards of Journal of Machine Learning, Annals of Statistics, Annual Review of Statistics.