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 and a three-year NSF grant on high-dimensional covariance estimation. He serves as an associate editor of Biometrika and Biostatistics. Before joining USC, he was an assistant professor at Cornell.