I am a research scientist at Disney Research Zurich heading the Analytics group. I am particularly interested in deep learning, randomized algorithms and optimization for large scale learning.
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.
Until August 2015 I was a postdoctoral researcher and lecturer in the Institute of Machine Learning at ETH Zurich.
Open positions: We have postdoc and internship openings for talented researchers in the fields of machine learning, optimization and statistics. Particularly in the areas of deep learning for natural language understanding and reinforcement learning. If that sounds like your thing, please send me a copy of your CV.
Christina Heinze, Brian McWilliams, Nicolai Meinshausen.
Ivan Ovinnikov, Brian McWilliams, Dmitry Laptev, Joachim Buhmann.
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim Buhmann.
Christina Heinze, Brian McWilliams, Nicolai Meinshausen and Gabriel Krummenacher. arXiv.
Software (maintained by Christina).
Aurelien Lucchi, Brian McWilliams and Thomas Hofmann. arXiv.
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Markus Gross, Luc Van Gool and Alexander Sorkine-Hornung. CVPR 2016. Project page.
Christina Heinze, Brian McWilliams and Nicolai Meinshausen. AISTATS 2016. arXiv. Software (maintained by Christina).
Thomas Hofmann, Aurelien Lucchi, Simon Lacoste-Julien and Brian McWilliams. NIPS 28. arXiv.
Christina Heinze, Brian McWilliams and Nicolai Meinshausen. NIPS WS on Learning and privacy with incomplete data and weak supervision.
Barbora Micenková, Brian McWilliams and Ira Assent. arXiv.
Gabriel Krummenacher and Brian McWilliams. 7th NIPS Workshop on Optimization for Machine Learning. pdf.
With Gabriel Krummenacher, Mario Lučić and Joachim M. Buhmann. NIPS 27. arXiv. Software (maintained by Gabriel). Slides and video from the Zuri ML meetup.
Barbora Micenková, Brian McWilliams and Ira Assent. KDD Workshop on Outlier Detection & Description under Data Diversity (ODD²). pdf.
With David Balduzzi and Joachim M. Buhmann. In Advances in Neural Information Processing Systems (NIPS) 26. arXiv. Matlab code. Poster.
With David Balduzzi. SPARS 2013.
2012 and earlier
- Projection based models for high dimensional data. Ph.D. thesis.
- Multi-view predictive partitioning in high dimensions. With Giovanni Montana Statistical Analysis and Data Mining (2012). 5: 304-321. arXiv.
- Predictive subspace clustering.
With Giovanni Montana. In Proceedings of the 10th International Conference on Machine Learning and Applications (2011), 247-252. pdf.
- A PRESS statistic for two-block partial least squares regression.
With Giovanni Montana. In Proceedings of the 10th Conference on Computational Intelligence UK (2010), Colchester. pdf.
- Sparse partial least squares for on-line variable selection in multivariate data streams.
With Giovanni Montana Statistical Analysis and Data Mining (2010). 3: 170-193. pdf
- Predictive modeling with high-dimensional data streams: an on-line variable selection approach.
With Giovanni Montana. Signal Processing with Adaptive Sparse Structured Representations (SPARS) 2009.
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