Bin Yu

Bin Yu is Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley. She was a Guggenheim Fellow in 2006 and is a member of the National Academy of Sciences. Her publications cover a wide range of research on empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference, and interdisciplinary data problems. Her current research interests focus on statistics and machine learning theory, methodologies, and algorithms for solving high-dimensional data problems in neuroscience, genomics, remote sensing and text mining. 

In particular, with the Gallant’s lab at Berkeley, Yu's group developed predictive models of brain functional MRI (fMRI) that are used for decoding of movies, dubbed “mind reading” by media in 2011. She is interested in understanding heterogeneity in big genomics data  (e.g. spatial gene expression) in order to construct localized gene networks (hence localized gene functions). She is also interested in estimating heterogeneous treatment effects in precision medicine and digital experiments via non-linear adjustment methods such as Lasso, Random Forests and Deep Learning. She is very experienced with image data from diverse fields of signal processing, remote sensing, neuroscience and genomics, and believes in cross-fertilization of ideas across disciplines and teamwork for innovative science.