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. Most of my ongoing work is motivated by some form of (scientific) knowledge acquisition, and the interrelated roles of models, data, inference, and prediction. Some recent examples:

  • Uses and benefits of symmetry in modeling, inference, and computation.
  • Statistical hypothesis tests for symmetry.
  • Integrating probability, statistics, and data with scientific models.
  • The role of causality, broadly construed, in learning from observations of and interactions with the world.
I also collaborate with researchers in the sciences on statistical problems arising in their research.
My research is supported by funding from NSERC, CANSSI, and UBC, and by computational resources and services provided by Advanced Research Computing at UBC.

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)



  • Non-parametric Hypothesis Tests for Distributional Group Symmetry
    K. Chiu and B. Bloem-Reddy
    [arxiv] [pdf] [code]

Published and to appear

  • Indeterminacy in Generative Models: Characterization and Strong Identifiability
    Q. Xi and B. Bloem-Reddy
    AISTATS 2023 (Oral presentation)
    [aistats] [arxiv]
  • Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures"
    B. Bloem-Reddy
    Journal of the Royal Statistical Society, Series B (Statistical Methodology), 79(5)
    [jrss b] [pdf] [slides from discussion at RSS meeting]

In progress (comments welcome)

Workshop contributions

(Most of these have a more fully developed counterpart above)

  • Generalization Bounds for Invariant Neural Networks
    C. Lyle, B. Bloem-Reddy, Y. Gal, M. Kwiatkowska
    NeurIPS 2019 Workshop on Machine Learning with Guarantees



  • Fall 2023: STAT 460/560, Theory of Statistical Inference I
  • Spring 2024: STAT 305, Introduction to Statistical Inference
    Course website on Canvas

Past courses

  • Fall 2019, 2020, 2021, 2022: STAT 547C, Topics in Probability
    [course website: 2021, 2022]
  • Summer 2020, Spring 2020, 2021, 2022, 2023: 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)