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. My recent research has focused on developing methods for evolving networks whose history is unobserved; on distributional limits of preferential attachment networks; and on uses of symmetry in statistics, computation, and machine learning. My work has used and developed methods in Bayesian nonparametrics, sequential Monte Carlo, and probabilistic symmetries.

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)