I'm a staff research scientist at Google DeepMind working on problems related to artificial intelligence. My group's research is focused on figuring out how we can get computers to learn with less supervision.
See coverage on Quanta, Ars Technica, and by Stephen Colbert for an overview of our recent work.
Previously I was a post-doctoral researcher at Microsoft Research Cambridge. I did my PhD at the University of Edinburgh, where I was a Carnegie scholar. My supervisor was Christopher Williams.
During my PhD I was also a visiting researcher at Oxford University working with Andrew Zisserman. I also did an internship at Microsoft Research working with John Winn.
I helped organise the PASCAL Visual Object Classes challenge from 2012 to 2015.
Currently Highlighting
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Neural Scene Representation and Rendering Science, 14 Jun 2018.
Extensions of the Generative Query Network: -
Synthesizing Programs for Images using Reinforced Adversarial Learning International Conference on Machine Learning (ICML), Stockholm, Sweden, 27 Mar 2018.
All Publications
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Neural Processes International Conference on Machine Learning (ICML), Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden. -
Conditional Neural Processes International Conference on Machine Learning (ICML), Stockholm, Sweden. -
Encoding Spatial Relations from Natural Language -
Learning models for visual 3D localization with implicit mapping -
Consistent Generative Query Networks -
A Probabilistic U-Net for Segmentation of Ambiguous Images -
Neural Scene Representation and Rendering Science, doi:10.1126/science.aar6170 -
Generative Temporal Models with Spatial Memory for Partially Observed Environments International Conference on Machine Learning (ICML), Stockholm, Sweden. -
Synthesizing Programs for Images using Reinforced Adversarial Learning International Conference on Machine Learning (ICML), Stockholm, Sweden. -
Machine Theory of Mind International Conference on Machine Learning (ICML), Stockholm, Sweden. -
Kickstarting Deep Reinforcement Learning -
Learning and Querying Fast Generative Models for Reinforcement Learning -
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions International Conference on Learning Representations (ICLR), Vancouver, Canada. -
Emergence of Locomotion Behaviours in Rich Environments -
Unsupervised Learning of 3D Structure from Images Neural Information Processing Systems (NIPS), Barcelona, Spain. -
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models Neural Information Processing Systems (NIPS), Barcelona, Spain. -
Reinforced Variational Inference Neural Information Processing Systems (NIPS), Workshop on Advances in Approximate Bayesian Inference, Montréal, Canada. Oral presentation. -
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages Uncertainty in Artificial Intelligence (UAI), Amsterdam, The Netherlands. -
Just-In-Time Kernel Regression for Expectation Propagation International Conference on Machine Learning (ICML), Workshop on Large Scale Kernel Learning, Lille, France. -
Consensus Message Passing for Layered Graphical Models International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA. -
Just-In-Time Learning for Fast and Flexible Inference Neural Information Processing Systems (NIPS), Montreal, Quebec, Canada. -
The PASCAL Visual Object Classes Challenge — a Retrospective International Journal of Computer Vision (IJCV), Springer. -
Assessing the Significance of Performance Differences on the PASCAL VOC Challenges via Bootstrapping Technical note. -
The Shape Boltzmann Machine: a Strong Model of Object Shape International Journal of Computer Vision (IJCV), Springer. CVPR special issue. -
A Generative Model for Parts-based Object Segmentation Neural Information Processing Systems (NIPS), Lake Tahoe, California, USA. -
The Shape Boltzmann Machine: a Strong Model of Object Shape Computer Vision and Pattern Recognition (CVPR), Rhode Island, USA. Oral presentation. -
Factored Shapes and Appearances for Parts-based Object Understanding British Machine Vision Conference (BMVC), Dundee, UK. Oral presentation.
Theses
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Generative Probabilistic Models for Object Segmentation The University of Edinburgh, PhD thesis. -
Evolving Robust Control Strategies for Simulated Animats The University of Edinburgh, BEng dissertation.
Selected talks
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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models - Beyond Supervised Learning Workshop, ICCV, Venice, Italy (October 2017)
- Sharif University of Technology, Tehran, Iran (December 2016)
- University of Tehran, Tehran, Iran (December 2016)
- Data Learning and Inference (DALI) Meeting, RNN Workshop, Sestri Levante, Italy (March 2016)
- Robotics Research Seminar, Oxford Universty, Oxford, UK (November 2016)
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Modern Artificial Intelligence - Invited Lecture, Digikala, Tehran, Iran (December 2016)
- Machine Learning Tutorial Series, Imperial College London, London, UK (October 2016)
- Sharif University of Technology, Tehran, Iran (December 2015)
- Shahid Beheshti University, Tehran, Iran (December 2015)
- Tehran University, Tehran, Iran (December 2015)
- Campus London, London, UK (August 2015)
- Robotic's Society Lecture Series, King's College London, London, UK (March 2015)
- COMPM041 Guest Lecture, University College London, London, UK (March 2015)
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Artificial Intelligence and Computer Aided Design - Grimshaw Architects, London, UK (January 2015)
- Colloquium Seminar Series, University College London, London, UK (January 2015)
- Sharif University of Technology, Kish, Iran (December 2014)
- Faculty of Fine Arts, University of Tehran, Tehran, Iran (December 2014)
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Extensions to Message Passing Inference - Sharif University of Technology, Kish, Iran (December 2014)
- Microsoft Research, Cambridge, UK (September 2014)
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Boltzmann Machines and their Extensions - Heriot-Watt University, Edinburgh, UK (March 2013)
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Generative Models of Images of Objects - Sharif University of Technology, Tehran, Iran (January 2013)
- Redwood Institute at the University of California at Berkeley, USA (December 2012)
- Bosch Research and Technology North America, Palo Alto, USA (December 2012)
- Toyota Technological Institute, University of Chicago, Chicago, USA (June 2012)
- New York University, New York, USA (June 2012)
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The Shape Boltzmann Machine: a Strong Model of Object Shape - Computer Vision and Pattern Recognition (CVPR), Providence, USA (June 2012)
- Rank Prize Symposium on Machine Learning and Vision, Windermere, UK (March 2012)
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Factored Shapes and Appearances for Parts-based Object Understanding - British Machine Vision Conference, Dundee, UK (August 2011)
- ACM Seminar, University of Tehran, Tehran, Iran (September 2011)
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Hierarchical Probabilistic Models for Object Segmentation - Canadian Institute for Advanced Research Summer School, Toronto, Canada (August 2010)
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High Dynamic Range Lighting on the Xbox 360 - TechMeetup, Edinburgh, UK (April 2010)
Other things
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The Zeroth Rule of Presentations -
14 Oct 2012
- Guide
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Patterns for Research in Machine Learning -
18 Jul 2012
- Guide
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Thoughts on the Role of Supervision in Computer Vision -
18 May 2011
- Slides
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A Visual Summary of the PASCAL 2009 Dataset -
01 Jan 2010
- Summary
Workshops
I co-organised the NIPS 2017 workshop on Machine Learning for Creativity and Design, that was held in Long Beach, California on December 8, 2017.
I co-organised the NIPS 2015 workshop on Black Box Learning and Inference, that was held in Montréal, Canada on December 12, 2015.
