Jacob Bien


Assistant Professor
Data Sciences and Operations (Statistics group)
Marshall School of Business
University of Southern California

I work on problems in statistical machine learning and in particular the development of novel methods that balance flexibility and interpretability for analyzing complex data. My goal is to develop methods that are of direct use to scientists and others with large datasets. A particular interest of mine is in using convex optimization to tackle the challenges of high-dimensional data. When I develop a new method, I take a broad approach to its study, considering practical, computational, and theoretical aspects.

New: I have just released the simulator, an R package that streamlines the process of performing simulations by creating a common infrastructure that can be easily used and reused across projects.


  • PhD in Statistics, Stanford University, 2012, advised by Rob Tibshirani.

  • MS in Statistics, Stanford University, 2007.

  • BS in Physics, Stanford University (secondary major in Mathematics), 2006.

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