Benjamin Bloem-Reddy

NEWS: Along with Brooks Paige, Matt J. Kusner, Rich Caruana, Tom Rainforth, and Yee Whye Teh, I am organizing a NIPS 2018 workshop: Critiquing and Correcting Trends in Machine Learning. If you'll be at NIPS on December 7, please check it out.

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.

My research focuses on probabilistic and statistical analysis of discrete data. In particular, I have worked on probabilistic models 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 incorporating probabilistic symmetry into neural networks, and 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

  • Neural network models of exchangeable sequences
    B. Bloem-Reddy and Y. W. Teh
    Submitted to the NIPS 2018 Workshop on Bayesian Deep Learning
    [pdf]