Jacob Bien, Ph.D.

Assistant Professor of Data Sciences and Operations, Marshall School of Business, University of Southern CaliforniaJacob Bien, Ph.D.’s Website

Project: Statistical Network Inference and Time Series Analysis of Marine Ecosystems

Understanding ocean ecosystems critically hinges on our ability to uncover the complex relationships and interactions between living organisms and geochemical properties as they vary across space and time. As the scale and richness of marine data sets increase, the research questions being investigated become more ambitious and therefore more complicated to answer in a rigorous way. It becomes essential to address the methodological challenges associated with analyzing such datasets in a statistically sound and computationally efficient way.

In this project, we will develop statistical tools that can be used to infer the temporal and spatial interactions between large numbers of entities (e.g., microbial species) and the effect of other covariates (e.g., salinity and temperature) on these interactions. Recent developments from the high-dimensional statistics literature on graphical modeling and time series analysis will play a central role in this work. Efficient code will be produced and made publicly and freely available for marine researchers.


Jacob Bien is an assistant professor in the Department of Data Sciences and Operations in the Marshall School of Business at the University of Southern California (USC). He received a B.S. in physics and a Ph.D. in statistics from Stanford University. Before joining USC, he was an assistant professor at Cornell University in the Department of Biological Statistics and Computational Biology and in the Department of Statistical Science. Dr. 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. Particular areas of focus include variable selection, clustering, prototype selection and the modeling of dependence in high-dimensional data. His work has been supported by the National Science Foundation, both through a CAREER award and through a grant on high-dimensional covariance estimation. He serves as an associate editor of Biometrika and Biostatistics.

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