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 grants from the Simons Foundation on developing new statistical methodology for oceanography. He serves as an associate editor of the Journal of the American Statistical Association and the Journal of the Royal Statistical Society (Series B), and he was previously an associate editor for Biometrika, the Journal of Computational and Graphical Statistics, and Biostatistics. Before joining USC, he was an assistant professor at Cornell.
Areas of Expertise
NEWS + EVENTS
Marshall Faculty Publications, Awards, and Honors: November 2023
We congratulate our distinguished faculty for their recently accepted and published research and awards.
Marshall Faculty Publications, Awards, and Honors: July 2023
We are proud to highlight the amazing Marshall faculty who have received awards this month for their groundbreaking work.
Marshall Faculty Publications, Awards, and Honors: May 2023 and Year-End Round-Up
We are thrilled to congratulate our faculty on recently accepted and published research, 2022-2023 teaching and research awards, and new chair appointments.
Comparing Trained and Untrained Probabilistic Ensemble Forecasts of COVID-19 Cases and Deaths in the United States
In recent work published in the International Journal of Forecasting, Professor Jacob Bien and his co-authors describe their efforts to evaluate the performance of different ensemble models to forecast cases and deaths as part of the US Covid-19 Forecast Hub, which was used by the CDC and various state health officials.
RESEARCH + PUBLICATIONS