Brian McWilliams

About me.

I am a research scientist at Disney Research Zurich heading the Analytics group. I am particularly interested in randomized algorithms and optimization for large scale learning.

Until August 2015 I was a postdoctoral researcher and lecturer in the Institute of Machine Learning at ETH Zurich.
I completed my Ph.D. in April 2012 in the Statistics Section of the Department of Mathematics Imperial College, London under the supervision of Giovanni Montana . Previously, I did the MSc. in Informatics specialising in Machine Learning, Neuroinformatics and Intelligent Robotics at the University of Edinburgh and a BEng. in Computer Systems Engineering at the University of Warwick.

Contact details.


June 2015

  • In September 2015 I will be heading down the road (it is a small world, afterall) to join Disney Research Zurich as a research scientist in the analytics group.

  • Christina Heinze has developed a great SPARK package for Loco and Dual-Loco. More information is available at the following Github page.
  • Papers.


  • DUAL-LOCO: Distributing Statistical Estimation Using Random Projections.
    Christina Heinze, Brian McWilliams and Nicolai Meinshausen. arXiv.
    Software (maintained by Christina).

  • A Variance Reduced Stochastic Newton Method.
    Aurelien Lucchi, Brian McWilliams and Thomas Hofmann. arXiv.

  • LOCO: Distributing Ridge Regression with Random Projections.
    Christina Heinze, Brian McWilliams, Nicolai Meinshausen and Gabriel Krummenacher. arXiv.
    Software (maintained by Christina).

  • Learning Representations for Outlier Detection on a Budget.
    Barbora Micenková, Brian McWilliams and Ira Assent. arXiv.

  • 2015

  • Variance Reduced Stochastic Gradient Descent with Neighbors.
    Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien and Brian McWilliams. To appear in NIPS 2015. arXiv.

  • 2014

  • RadaGrad: Random Projections for Adaptive Stochastic Optimization.
    Gabriel Krummenacher and Brian McWilliams. 7th NIPS Workshop on Optimization for Machine Learning. pdf.

  • Fast and Robust Least Squares Estimation in Corrupted Linear Models.
    With Gabriel Krummenacher, Mario Lučić and Joachim M. Buhmann. Advances in Neural Information Processing Systems (NIPS) 27.
  • Learning Outlier Ensembles: The Best of Both Worlds – Supervised and Unsupervised.
    Barbora Micenková, Brian McWilliams and Ira Assent. KDD Workshop on Outlier Detection & Description under Data Diversity (ODD²). pdf.

  • Subspace clustering of high-dimensional data: a predictive approach.
    With Giovanni Montana. Data Mining and Knowledge Discovery. 28(3): 736-772. arXiv.

  • 2013

  • Correlated random features for fast semi-supervised learning.
    With David Balduzzi and Joachim M. Buhmann. In Advances in Neural Information Processing Systems (NIPS) 26.
  • Pruning random features with correlated kitchen sinks (1 page abstract).
    With David Balduzzi. Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2013. (For poster see NIPS paper above).

  • 2012 and earlier


    Courses I am involved with.

    Fall Semester 2014

    I co-lectured Probabilistic Graphical Models for Image Analysis with Dr. Aurelien Lucchi.

    Spring Semester 2014

    I was head TA of Computational Intelligence Laboratory. This course is now taught by Prof. Hofmann and headed by Martin Jaggi and Aurelien Lucchi.


    The website for the probabilistic graphical models course that Dr. David Balduzzi and I taught at Uni Basel in summer 2013 is located here.



    Disney Research Zurich
    Stampfenbachstrasse 48
    8006 Zurich