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Jacob Bien's research focuses on statistical machine learning, particularly the development of novel methods that balance flexibility and interpretability while analyzing complex data. He combines ideas from convex optimization and statistics to develop methods for direct use by scientists working with large datasets. His work has been supported by an NSF CAREER award, a three-year NSF grant for high-dimensional covariance estimation, and an NIH R01 grant for methods for analyzing multi-view data, along with grants from the Simons Foundation aimed at developing new statistical methodologies in oceanography. He is a fellow of both the Institute of Mathematical Statistics and the American Statistical Association, and in 2025, he received the Dean’s Award for Research Impact from the Marshall School of Business. He has served as an associate editor for the Journal of the American Statistical Association and has previously held editorial roles for several leading journals in the field. Before joining USC, he was an assistant professor at Cornell University.
USC Marshall • Los Angeles, CA
Teaching advanced courses and conducting research in data sciences and operations.
Cornell University • Ithaca, NY
Conducted research and taught courses in statistics and machine learning.
GRE is NOT required for Master's applicants for 2025-2026.