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:

  • Uses and benefits of symmetry in statistics, computation, and machine learning.
  • Models and inference methods (SMC, MCMC) for evolving processes (e.g., networks, forest fires) whose history is partially or full unobserved.
I have a growing interest in causality and its interplay with knowledge and inference. 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

Research group

Current (alphabetical order)

Past (reverse chronological order)


Published and pre-prints

  • Indeterminacy in Latent Variable Models: Characterization and Strong Identifiability
    Q. Xi and B. Bloem-Reddy



  • Fall 2022: STAT 547C, Topics in Probability for Statistics
    [course website]


Spring 2022: STAT 305, STAT 547S: Topics on Symmetry in Statistics and ML

Past courses

  • Fall 2019, 2020, 2021: STAT 547C, Topics in Probability
    [course website: 2019, 2020, 2021]
  • Summer 2020, Spring 2020, Spring 2021: STAT 305, Introduction to Statistical Inference


  • 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)