**Brian McWilliams.**

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

I completed my Ph.D. in 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.

Until August 2015 I was a postdoctoral researcher and lecturer in the
Institute of Machine Learning at ETH Zurich.

**Open positions:**We have masters thesis projects in the area of deep learning and generative modelling available.

### News.

**May 2017**

Shattered Gradients and Neural Taylor Approximations accepted to ICML 2017!

**March 2017**

Paper with Pixar Animation Studios on denoising MC rendered images using kernel-predicting convnets accepted to SIGGRAPH 2017!

**Feb 2017**

*PriDE*(Private Distributed Estimation) is an algorithm for preserving differential privacy in distributed statistical estimation tasks.

*The Shattered Gradients Problem*describes the phenomenon of how gradients whiten as neural networks get deeper making optimization hard. We show why ResNets fix this issue.

**Nov 2016**

*Neural Taylor Approximation*. We provide the first convergence result for deep neural networks with ReLU activations.

**Aug 2016**

*RadaGrad!*our paper on scalable approximations to full-matrix AdaGrad using random projections is accepted to NIPS 2016!