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Jacob Bien

  • Professor of Data Sciences and Operations

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 is a fellow of the Institute of Mathematical Statistics and a fellow of the American Statistical Assocation. He serves as an associate editor of the Journal of the American Statistical Association and the Journal of the Royal Statistical Society (Series B); 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.

Jacob Bien
Press Inquiries: COMMUNICATIONS@MARSHALL.USC.EDU

Areas of Expertise

Analytics
Artificial Intelligence (AI)
Machine Learning
Optimization
Statistical Inference
Statistics

Departments

Data Sciences + Operations

NEWS + EVENTS

RESEARCH + PUBLICATIONS

COURSES

Spring 2025
  • DSO-699
    Advanced Data Science: Modeling, Computing & Optimization
    Core data science process. From data to model. From model to algorithm. From algorithm to output. From output to insight.
Fall 2024
  • BUAD-312
    Statistics and Data Science for Business
    Statistical methods for exploration, visualization, and analysis of large business datasets; simulation; hypothesis testing, multiple regression modeling, and confidence intervals. Coding taught through data applications. (Faster-paced version of BUAD-310g. Duplicates credit in BUAD 310g and the former ITP 251.)
Fall 2023
  • GSBA-524
    Data Science for Business
    Principles of probability theory and classical statistics applied to business decision problems; survey analysis, estimation and prediction methods, evaluation, and control techniques. Duplicates credit in GSBA 506ab, GSBA 516, GSBA 545.