Benjamin Bloem-Reddy

I am Assistant Professor of Statistics at the University of British Columbia. I work on problems in statistics and machine learning, with an emphasis on probabilistic approaches. Some recent examples:

  • Meaningful calibration of uncertainty in neural network models of conditional distributions and stochastic processes.
  • Uses and benefits of symmetry in statistics, computation, and machine learning.
  • Models and inference methods (SMC, MCMC) for evolving processes (e.g., networks) whose history is unobserved.
I also collaborate with researchers in the sciences on statistical problems arising in their research.

I was a PhD student with Peter Orbanz at Columbia and a postdoc with Yee Whye Teh in the CSML group at the University of Oxford. Before moving to statistics and machine learning, I studied physics at Stanford University and Northwestern University.

Contact: benbr at stat dot ubc dot ca
Office: Department of Statistics, Earth Sciences Building, Room 3168


  • Fall 2020: STAT 547C, Topics in Probability for Statistics
    [course website]

Past courses


  • Exchangeable random partitions and random discrete probability measures: a brief tour guided by the Dirichlet Process
    B. Bloem-Reddy
    Notes for a lecture given to Oxford PhD students (these are a work in progress)

Research group

Current students (alphabetical order)


  • Current UBC graduate students: Send me an email and we can talk.

  • Prospective graduate students: If you're interested in working with me, you should apply to the UBC Statistics Department; in your application research/personal statement, indicate that you are interested in working with me and explain how your research interests align with mine.