PhD, Statistics, Stanford (advisor: Robert Tibshirani); BS, Physics, Stanford
Jacob Bien's research focuses on statistical machine learning and in particular the development of novel methods that balance flexibility and interpretability for analyzing complex data. He combines ideas from convex optimization and statistics to develop methods that are of direct use to scientists and others with large datasets. His work has been supported by an NSF CAREER award, a three-year NSF grant on high-dimensional covariance estimation, an NIH R01 grant on methods for multi-view data, and a grant from the Simons Foundation on developing new statistical methodology for oceanography. He serves as an associate editor of Biometrika and the Journal of Computational and Graphical Statistics, and he was previously an associate editor for Biostatistics. Before joining USC, he was an assistant professor at Cornell.