Benjamin Bloem-Reddy

I am a Postdoctoral Research Assistant in Statistical Machine Learning in the CSML group, based in the Department of Statistics at the University of Oxford.

Along with Jessie Wu, I organize the CSML Seminar, held on Friday afternoons during term time. See the seminar website for more information.

My research focuses on probabilistic and statistical analysis of discrete data. In particular, I work on probabilistic models, estimation, and inference for objects like graphs, partitions, and permutations. Natural applications of these ideas arise in, for example, modeling networks or text, and in matrix factorization. Recently, I have also worked on probabilistic programming, particularly in the context of Bayesian nonparametric models. I am generally interested in all aspects of machine learning, both theoretical and applied.

Previously, I completed my Ph.D. in Statistics at Columbia University, where I was advised by Peter Orbanz. I completed my B.S. in Physics at Stanford University, and my M.S. in Physics at Northwestern University, where I worked in the lab of William P. Halperin. Prior to studying at Columbia, I worked for three years as a research analyst at The Brattle Group in Washington, D.C.

Contact: benjamin dot bloem-reddy at stats.ox.ac.uk
Office: Department of Statistics, Room 1.06

Papers

  • Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks.
    B. Bloem-Reddy, A. Foster, E. Mathieu, Y. W. Teh
    To appear, UAI 2018.

Selected talks

  • Preferential attachment, vertex arrival times, and probabilistic symmetries in random graphs.
    University of Bristol Statistics Seminar. Bristol, UK. 13 October 2017.
  • Random walk models of networks: modeling and inferring complex dependence.
    Workshop on Bayesian Methods for Networks. Isaac Newton Institute, Cambridge, UK. July 2016.
    [video]
  • Slice Sampling on Hamiltonian Trajectories.
    ICML. New York. June 2016.
    [video]